This glossary defines general machine learning terms, plus terms specific to TensorFlow. Show
AA/B testingA statistical way of comparing two (or more) techniques—the A and the B. Typically, the A is an existing technique, and the B is a new technique. A/B testing not only determines which technique performs better but also whether the difference is statistically significant. A/B testing usually compares a single metric on two techniques; for example, how does model accuracy compare for two techniques? However, A/B testing can also compare any finite number of metrics. accuracyThe number of correct classification predictions divided by the total number of predictions. That is: $$\text{Accuracy} = \frac{\text{correct predictions}} {\text{correct predictions + incorrect predictions }}$$ For example, a model that made 40 correct predictions and 10 incorrect predictions would have an accuracy of: $$\text{Accuracy} = \frac{\text{40}} {\text{40 + 10}} = \text{80%}$$ Binary classification provides specific names for the different categories of correct predictions and incorrect predictions. So, the accuracy formula for binary classification is as follows: $$\text{Accuracy} = \frac{\text{TP} + \text{TN}} {\text{TP} + \text{TN} + \text{FP} + \text{FN}}$$ where:
Compare and contrast accuracy with precision and recall. Click the icon for additional notes.Although a valuable metric for some situations, accuracy is highly misleading for others. Notably, accuracy is usually a poor metric for evaluating classification models that process class-imbalanced datasets. For example, suppose snow falls only 25 days per century in a certain subtropical city. Since days without snow (the negative class) vastly outnumber days with snow (the positive class), the snow dataset for this city is class-imbalanced. Imagine a binary classification model that is supposed to predict either snow or no snow each day but simply predicts "no snow" every day. This model is highly accurate but has no predictive power. The following table summarizes the results for a century of predictions:
The accuracy of this model is therefore: accuracy = (TP + TN) / (TP + TN + FP + FN) accuracy = (0 + 36500) / (0 + 36500 + 25 + 0) = 0.9993 = 99.93% Although 99.93% accuracy seems like very a impressive percentage, the model actually has no predictive power. Precision and recall are usually more useful metrics than accuracy for evaluating models trained on class-imbalanced datasets. actionIn reinforcement learning, the mechanism by which the agent transitions between states of the environment. The agent chooses the action by using a policy. activation functionA function that enables neural networks to learn nonlinear (complex) relationships between features and the label. Popular activation functions include:
The plots of activation functions are never single straight lines. For example, the plot of the ReLU activation function consists of two straight lines:
A plot of the sigmoid activation function looks as follows:
Click the icon to see an example.In a neural network, activation functions manipulate the weighted sum of all the inputs to a neuron. To calculate a weighted sum, the neuron adds up the products of the relevant values and weights. For example, suppose the relevant input to a neuron consists of the following:
The weighted sum is therefore: weighted sum = (2)(-1.3) + (-1)(0.6) + (3)(0.4) = -2.0 Suppose the designer of this neural network chooses the sigmoid function to be the activation function. In that case, the neuron calculates the sigmoid of -2.0, which is approximately 0.12. Therefore, the neuron passes 0.12 (rather than -2.0) to the next layer in the neural network. The following figure illustrates the relevant part of the process:
active learningA training approach in which the algorithm chooses some of the data it learns from. Active learning is particularly valuable when labeled examples are scarce or expensive to obtain. Instead of blindly seeking a diverse range of labeled examples, an active learning algorithm selectively seeks the particular range of examples it needs for learning. AdaGradA sophisticated gradient descent algorithm that rescales the gradients of each parameter, effectively giving each parameter an independent learning rate. For a full explanation, see this paper. agentIn reinforcement learning, the entity that uses a policy to maximize the expected return gained from transitioning between states of the environment. agglomerative clusteringSee hierarchical clustering. anomaly detectionThe process of identifying outliers. For example, if the mean for a certain feature is 100 with a standard deviation of 10, then anomaly detection should flag a value of 200 as suspicious. ARAbbreviation for augmented reality. area under the PR curveSee PR AUC (Area under the PR Curve). area under the ROC curveSee AUC (Area under the ROC curve). artificial general intelligenceA non-human mechanism that demonstrates a broad range of problem solving, creativity, and adaptability. For example, a program demonstrating artificial general intelligence could translate text, compose symphonies, and excel at games that have not yet been invented. artificial intelligenceA non-human program or model that can solve sophisticated tasks. For example, a program or model that translates text or a program or model that identifies diseases from radiologic images both exhibit artificial intelligence. Formally, machine learning is a sub-field of artificial intelligence. However, in recent years, some organizations have begun using the terms artificial intelligence and machine learning interchangeably. attentionAny of a wide range of neural network architecture mechanisms that aggregate information from a set of inputs in a data-dependent manner. A typical attention mechanism might consist of a weighted sum over a set of inputs, where the weight for each input is computed by another part of the neural network. Refer also to self-attention and multi-head self-attention, which are the building blocks of Transformers. attributeSynonym for feature. In machine learning fairness, attributes often refer to characteristics pertaining to individuals. attribute samplingA tactic for training a decision forest in which each decision tree considers only a random subset of possible features when learning the condition. Generally, a different subset of features is sampled for each node. In contrast, when training a decision tree without attribute sampling, all possible features are considered for each node. AUC (Area under the ROC curve)A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes from negative classes. The closer the AUC is to 1.0, the better the model's ability to separate classes from each other. For example, the following illustration shows a classifier model that separates positive classes (green ovals) from negative classes (purple rectangles) perfectly. This unrealistically perfect model has an AUC of 1.0:
Conversely, the following illustration shows the results for a classifier model that generated random results. This model has an AUC of 0.5:
Yes, the preceding model has an AUC of 0.5, not 0.0. Most models are somewhere between the two extremes. For instance, the following model separates positives from negatives somewhat, and therefore has an AUC somewhere between 0.5 and 1.0:
AUC ignores any value you set for classification threshold. Instead, AUC considers all possible classification thresholds. Click the icon to learn about the relationship between AUC and ROC curves.AUC represents the area under an ROC curve. For example, the ROC curve for a model that perfectly separates positives from negatives looks as follows:
AUC is the area of the gray region in the preceding illustration. In this unusual case, the area is simply the length of the gray region (1.0) multiplied by the width of the gray region (1.0). So, the product of 1.0 and 1.0 yields an AUC of exactly 1.0, which is the highest possible AUC score. Conversely, the ROC curve for a classifier that can't separate classes at all is as follows. The area of this gray region is 0.5. A more typical ROC curve looks approximately like the following: It would be painstaking to calculate the area under this curve manually, which is why a program typically calculates most AUC values. Click the icon for a more formal definition of AUC.AUC is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive. augmented realityA technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view. automation biasWhen a human decision maker favors recommendations made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors. average precisionA metric for summarizing the performance of a ranked sequence of results. Average precision is calculated by taking the average of the precision values for each relevant result (each result in the ranked list where the recall increases relative to the previous result). See also Area under the PR Curve. axis-aligned conditionIn a decision tree, a condition that involves only a single feature. For example, if area is a feature, then the following is an axis-aligned condition: area > 200 Contrast with oblique condition. BbackpropagationThe algorithm that implements gradient descent in neural networks. Training a neural network involves many iterations of the following two-pass cycle:
Neural networks often contain many neurons across many hidden layers. Each of those neurons contribute to the overall loss in different ways. Backpropagation determines whether to increase or decrease the weights applied to particular neurons. The learning rate is a multiplier that controls the degree to which each backward pass increases or decreases each weight. A large learning rate will increase or decrease each weight more than a small learning rate. In calculus terms, backpropagation implements calculus' chain rule. That is, backpropagation calculates the partial derivative of the error with respect to each parameter. For more details, see this tutorial in Machine Learning Crash Course. Years ago, ML practitioners had to write code to implement backpropagation. Modern ML APIs like TensorFlow now implement backpropagation for you. Phew! baggingA method to train an ensemble where each constituent model trains on a random subset of training examples sampled with replacement. For example, a random forest is a collection of decision trees trained with bagging. The term bagging is short for bootstrap aggregating. bag of wordsA representation of the words in a phrase or passage, irrespective of order. For example, bag of words represents the following three phrases identically:
Each word is mapped to an index in a sparse vector, where the vector has an index for every word in the vocabulary. For example, the phrase the dog jumps is mapped into a feature vector with non-zero values at the three indices corresponding to the words the, dog, and jumps. The non-zero value can be any of the following:
baselineA model used as a reference point for comparing how well another model (typically, a more complex one) is performing. For example, a logistic regression model might serve as a good baseline for a deep model. For a particular problem, the baseline helps model developers quantify the minimal expected performance that a new model must achieve for the new model to be useful. batchThe set of examples used in one training iteration. The batch size determines the number of examples in a batch. See epoch for an explanation of how a batch relates to an epoch. batch normalizationNormalizing the input or output of the activation functions in a hidden layer. Batch normalization can provide the following benefits:
batch sizeThe number of examples in a batch. For instance, if the batch size is 100, then the model processes 100 examples per iteration. The following are popular batch size strategies:
Bayesian neural networkA probabilistic neural network that accounts for uncertainty in weights and outputs. A standard neural network regression model typically predicts a scalar value; for example, a model predicts a house price of 853,000. In contrast, a Bayesian neural network predicts a distribution of values; for example, a model predicts a house price of 853,000 with a standard deviation of 67,200. A Bayesian neural network relies on Bayes' Theorem to calculate uncertainties in weights and predictions. A Bayesian neural network can be useful when it is important to quantify uncertainty, such as in models related to pharmaceuticals. Bayesian neural networks can also help prevent overfitting. Bayesian optimizationA probabilistic regression model technique for optimizing computationally expensive objective functions by instead optimizing a surrogate that quantifies the uncertainty via a Bayesian learning technique. Since Bayesian optimization is itself very expensive, it is usually used to optimize expensive-to-evaluate tasks that have a small number of parameters, such as selecting hyperparameters. Bellman equationIn reinforcement learning, the following identity satisfied by the optimal Q-function: \[Q(s, a) = r(s, a) + \gamma \mathbb{E}_{s'|s,a} \max_{a'} Q(s', a')\] Reinforcement learning algorithms apply this identity to create Q-learning via the following update rule: \[Q(s,a) \gets Q(s,a) + \alpha \left[r(s,a) + \gamma \displaystyle\max_{\substack{a_1}} Q(s’,a’) - Q(s,a) \right] \] Beyond reinforcement learning, the Bellman equation has applications to dynamic programming. See the Wikipedia entry for Bellman Equation. BERT (Bidirectional Encoder Representations from Transformers)A model architecture for text representation. A trained BERT model can act as part of a larger model for text classification or other ML tasks. BERT has the following characteristics:
BERT's variants include:
See Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing for an overview of BERT. bias (ethics/fairness)1. Stereotyping, prejudice or favoritism towards some things, people, or groups over others. These biases can affect collection and interpretation of data, the design of a system, and how users interact with a system. Forms of this type of bias include:
2. Systematic error introduced by a sampling or reporting procedure. Forms of this type of bias include:
Not to be confused with the bias term in machine learning models or prediction bias. bias (math) or bias termAn intercept or offset from an origin. Bias is a parameter in machine learning models, which is symbolized by either of the following:
For example, bias is the b in the following formula: $$y' = b + w_1x_1 + w_2x_2 + … w_nx_n$$ In a simple two-dimensional line, bias just means "y-intercept." For example, the bias of the line in the following illustration is 2.
Bias exists because not all models start from the origin (0,0). For example, suppose an amusement park costs 2 Euros to enter and an additional 0.5 Euro for every hour a customer stays. Therefore, a model mapping the total cost has a bias of 2 because the lowest cost is 2 Euros. Bias is not to be confused with bias in ethics and fairness or prediction bias. bigramAn N-gram in which N=2. bidirectionalA term used to describe a system that evaluates the text that both precedes and follows a target section of text. In contrast, a unidirectional system only evaluates the text that precedes a target section of text. For example, consider a masked language model that must determine probabilities for the word or words representing the underline in the following question:
A unidirectional language model would have to base its probabilities only on the context provided by the words "What", "is", and "the". In contrast, a bidirectional language model could also gain context from "with" and "you", which might help the model generate better predictions. bidirectional language modelA language model that determines the probability that a given token is present at a given location in an excerpt of text based on the preceding and following text. binary classificationA type of classification task that predicts one of two mutually exclusive classes:
For example, the following two machine learning models each perform binary classification:
Contrast with multi-class classification. See also logistic regression and classification threshold. binary conditionIn a decision tree, a condition that has only two possible outcomes, typically yes or no. For example, the following is a binary condition: temperature >= 100 Contrast with non-binary condition. binningSynonym for bucketing. BLEU (Bilingual Evaluation Understudy)A score between 0.0 and 1.0, inclusive, indicating the quality of a translation between two human languages (for example, between English and Russian). A BLEU score of 1.0 indicates a perfect translation; a BLEU score of 0.0 indicates a terrible translation. boostingA machine learning technique that iteratively combines a set of simple and not very accurate classifiers (referred to as "weak" classifiers) into a classifier with high accuracy (a "strong" classifier) by upweighting the examples that the model is currently misclassifying. bounding boxIn an image, the (x, y) coordinates of a rectangle around an area of interest, such as the dog in the image below.
broadcastingExpanding the shape of an operand in a matrix math operation to dimensions compatible for that operation. For instance, linear algebra requires that the two operands in a matrix addition operation must have the same dimensions. Consequently, you can't add a matrix of shape (m, n) to a vector of length n. Broadcasting enables this operation by virtually expanding the vector of length n to a matrix of shape (m, n) by replicating the same values down each column. For example, given the following definitions, linear algebra prohibits A+B because A and B have different dimensions:
However, broadcasting enables the operation A+B by virtually expanding B to:
Thus, A+B is now a valid operation:
See the following description of broadcasting in NumPy for more details. bucketingConverting a single feature into multiple binary features called buckets or bins, typically based on a value range. The chopped feature is typically a continuous feature. For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete buckets, such as:
The model will treat every value in the same bucket identically. For example, the values Click the icon for additional notes.If you represent temperature as a continuous feature, then the model treats temperature as a single feature. If you represent temperature as three buckets, then the model treats each bucket as a separate feature. That is, a model can learn separate relationships of each bucket to the label. For example, a linear regression model can learn separate weights for each bucket. Increasing the number of buckets makes your model more complicated by increasing the number of relationships that your model must learn. For example, the cold, temperate, and warm buckets are essentially three separate features for your model to train on. If you decide to add two more buckets--for example, freezing and hot--your model would now have to train on five separate features. How do you know how many buckets to create, or what the ranges for each bucket should be? The answers typically require a fair amount of experimentation. Ccalibration layerA post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels. candidate generationThe initial set of recommendations chosen by a recommendation system. For example, consider a bookstore that offers 100,000 titles. The candidate generation phase creates a much smaller list of suitable books for a particular user, say 500. But even 500 books is way too many to recommend to a user. Subsequent, more expensive, phases of a recommendation system (such as scoring and re-ranking) reduce those 500 to a much smaller, more useful set of recommendations. candidate samplingA training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence). The idea is that the negative classes can learn from less frequent negative reinforcement as long as positive classes always get proper positive reinforcement, and this is indeed observed empirically. The motivation for candidate sampling is a computational efficiency win from not computing predictions for all negatives. categorical dataFeatures having a specific set of possible values. For example, consider a
categorical feature named
By representing Categorical features are sometimes called discrete features. Contrast with numerical data. causal language modelSynonym for unidirectional language model. See bidirectional language model to contrast different directional approaches in language modeling. centroidThe center of a cluster as determined by a k-means or k-median algorithm. For instance, if k is 3, then the k-means or k-median algorithm finds 3 centroids. centroid-based clusteringA category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms. checkpointData that captures the state of a model's parameters at a particular training iteration. Checkpoints enable exporting model weights, or performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). classA category that a label can belong to. For example:
A classification model predicts a class. In contrast, a regression model predicts a number rather than a class. classification modelA model whose prediction is a class. For example, the following are all classification models:
In contrast, regression models predict numbers rather than classes. Two common types of classification models are:
classification thresholdIn a binary classification, a number between 0 and 1 that converts the raw output of a logistic regression model into a prediction of either the positive class or the negative class. Note that the classification threshold is a value that a human chooses, not a value chosen by model training. A logistic regression model outputs a raw value between 0 and 1. Then:
For example, suppose the classification threshold is 0.8. If the raw value is 0.9, then the model predicts the positive class. If the raw value is 0.7, then the model predicts the negative class. The choice of classification threshold strongly influences the number of false positives and false negatives. Click the icon for additional notes.As models or datasets evolve, engineers sometimes also change the classification threshold. When the classification threshold changes, positive class predictions can suddenly become negative classes and vice-versa. For example, consider a binary classification disease prediction model. Suppose that when the system runs in the first year:
Therefore, the system diagnoses the positive class. (The patient gasps, "Oh no! I'm sick!") A year later, perhaps the values now look as follows:
Therefore, the system now reclassifies that patient as the negative class. ("Happy day! I'm not sick.") Same patient. Different diagnosis. class-imbalanced datasetA dataset for a classification problem in which the total number of labels of each class differs significantly. For example, consider a binary classification dataset whose two labels are divided as follows:
The ratio of negative to positive labels is 100,000 to 1, so this is a class-imbalanced dataset. In contrast, the following dataset is not class-imbalanced because the ratio of negative labels to positive labels is relatively close to 1:
Multi-class datasets can also be class-imbalanced. For example, the following multi-class classification dataset is also class-imbalanced because one label has far more examples than the other two:
See also entropy, majority class, and minority class. clippingA technique for handling outliers by doing either or both of the following:
For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60. In this case, you could do the following:
Outliers can damage models, sometimes causing weights to overflow during training. Some outliers can also dramatically spoil metrics like accuracy. Clipping is a common technique to limit the damage. Gradient clipping forces gradient values within a designated range during training. Cloud TPUA specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform. clusteringGrouping related examples, particularly during unsupervised learning. Once all the examples are grouped, a human can optionally supply meaning to each cluster. Many clustering algorithms exist. For example, the k-means algorithm clusters examples based on their proximity to a centroid, as in the following diagram:
A human researcher could then review the clusters and, for example, label cluster 1 as "dwarf trees" and cluster 2 as "full-size trees." As another example, consider a clustering algorithm based on an example's distance from a center point, illustrated as follows:
co-adaptationWhen neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network's behavior as a whole. When the patterns that cause co-adaption are not present in validation data, then co-adaptation causes overfitting. Dropout regularization reduces co-adaptation because dropout ensures neurons cannot rely solely on specific other neurons. collaborative filteringMaking predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems. conditionIn a decision tree, any node that evaluates an expression. For example, the following portion of a decision tree contains two conditions:
A condition is also called a split or a test. Contrast condition with leaf. See also:
confirmation biasThe tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses. Machine learning developers may inadvertently collect or label data in ways that influence an outcome supporting their existing beliefs. Confirmation bias is a form of implicit bias. Experimenter's bias is a form of confirmation bias in which an experimenter continues training models until a preexisting hypothesis is confirmed. confusion matrixAn NxN table that summarizes the number of correct and incorrect predictions that a classification model made. For example, consider the following confusion matrix for a binary classification model:
The preceding confusion matrix shows the following:
The confusion matrix for a multi-class classification problem can help you identify patterns of mistakes. For example, consider the following confusion matrix for a 3-class multi-class classification model that categorizes three different iris types (Virginica, Versicolor, and Setosa). When the ground truth was Virginica, the confusion matrix shows that the model was far more likely to mistakenly predict Versicolor than Setosa:
As yet another example, a confusion matrix could reveal that a model trained to recognize handwritten digits tends to mistakenly predict 9 instead of 4, or mistakenly predict 1 instead of 7. Confusion matrices contain sufficient information to calculate a variety of performance metrics, including precision and recall. continuous featureA floating-point feature with an infinite range of possible values, such as temperature or weight. Contrast with discrete feature. convenience samplingUsing a dataset not gathered scientifically in order to run quick experiments. Later on, it's essential to switch to a scientifically gathered dataset. convergenceA state reached when loss values change very little or not at all with each iteration. For example, the following loss curve suggests convergence at around 700 iterations:
A model converges when additional training will not improve the model. In deep learning, loss values sometimes stay constant or nearly so for many iterations before finally descending. During a long period of constant loss values, you may temporarily get a false sense of convergence. See also early stopping. convex functionA function in which the region above the graph of the function is a convex set. The prototypical convex function is shaped something like the letter U. For example, the following are all convex functions:
In contrast, the following function is not convex. Notice how the region above the graph is not a convex set:
A strictly convex function has exactly one local minimum point, which is also the global minimum point. The classic U-shaped functions are strictly convex functions. However, some convex functions (for example, straight lines) are not U-shaped. Click the icon for a deeper look at the math.A lot of the common loss functions, including the following, are convex functions:
Many variations of gradient descent are guaranteed to find a point close to the minimum of a strictly convex function. Similarly, many variations of stochastic gradient descent have a high probability (though, not a guarantee) of finding a point close to the minimum of a strictly convex function. The sum of two convex functions (for example, L2 loss + L1 regularization) is a convex function. Deep models are never convex functions. Remarkably, algorithms designed for convex optimization tend to find reasonably good solutions on deep networks anyway, even though those solutions are not guaranteed to be a global minimum. convex optimizationThe process of using mathematical techniques such as gradient descent to find the minimum of a convex function. A great deal of research in machine learning has focused on formulating various problems as convex optimization problems and in solving those problems more efficiently. For complete details, see Boyd and Vandenberghe, Convex Optimization. convex setA subset of Euclidean space such that a line drawn between any two points in the subset remains completely within the subset. For instance, the following two shapes are convex sets:
In contrast, the following two shapes are not convex sets:
convolutionIn mathematics, casually speaking, a mixture of two functions. In machine learning, a convolution mixes the convolutional filter and the input matrix in order to train weights. The term "convolution" in machine learning is often a shorthand way of referring to either convolutional operation or convolutional layer. Without convolutions, a machine learning algorithm would have to learn a separate weight for every cell in a large tensor. For example, a machine learning algorithm training on 2K x 2K images would be forced to find 4M separate weights. Thanks to convolutions, a machine learning algorithm only has to find weights for every cell in the convolutional filter, dramatically reducing the memory needed to train the model. When the convolutional filter is applied, it is simply replicated across cells such that each is multiplied by the filter. convolutional filterOne of the two actors in a convolutional operation. (The other actor is a slice of an input matrix.) A convolutional filter is a matrix having the same rank as the input matrix, but a smaller shape. For example, given a 28x28 input matrix, the filter could be any 2D matrix smaller than 28x28. In photographic manipulation, all the cells in a convolutional filter are typically set to a constant pattern of ones and zeroes. In machine learning, convolutional filters are typically seeded with random numbers and then the network trains the ideal values. convolutional layerA layer of a deep neural network in which a convolutional filter passes along an input matrix. For example, consider the following 3x3 convolutional filter:
The following animation shows a convolutional layer consisting of 9 convolutional operations involving the 5x5 input matrix. Notice that each convolutional operation works on a different 3x3 slice of the input matrix. The resulting 3x3 matrix (on the right) consists of the results of the 9 convolutional operations:
convolutional neural networkA neural network in which at least one layer is a convolutional layer. A typical convolutional neural network consists of some combination of the following layers:
Convolutional neural networks have had great success in certain kinds of problems, such as image recognition. convolutional operationThe following two-step mathematical operation:
For example, consider the following 5x5 input matrix:
Now imagine the following 2x2 convolutional filter:
Each convolutional operation involves a single 2x2 slice of the input matrix. For instance, suppose we use the 2x2 slice at the top-left of the input matrix. So, the convolution operation on this slice looks as follows:
A convolutional layer consists of a series of convolutional operations, each acting on a different slice of the input matrix. costSynonym for loss. co-trainingA semi-supervised learning approach particularly useful when all of the following conditions are true:
Co-training essentially amplifies independent signals into a stronger signal. For instance, consider a classification model that categorizes individual used cars as either Good or Bad. One set of predictive features might focus on aggregate characteristics such as the year, make, and model of the car; another set of predictive features might focus on the previous owner's driving record and the car's maintenance history. The seminal paper on co-training is Combining Labeled and Unlabeled Data with Co-Training by Blum and Mitchell. counterfactual fairnessA fairness metric that checks whether a classifier produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more sensitive attributes. Evaluating a classifier for counterfactual fairness is one method for surfacing potential sources of bias in a model. See "When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness" for a more detailed discussion of counterfactual fairness. coverage biasSee selection bias. crash blossomA sentence or phrase with an ambiguous meaning. Crash blossoms present a significant problem in natural language understanding. For example, the headline Red Tape Holds Up Skyscraper is a crash blossom because an NLU model could interpret the headline literally or figuratively. Click the icon for additional notes.Just to clarify that mysterious headline:
criticSynonym for Deep Q-Network. cross-entropyA generalization of Log Loss to multi-class classification problems. Cross-entropy quantifies the difference between two probability distributions. See also perplexity. cross-validationA mechanism for estimating how well a model would generalize to new data by testing the model against one or more non-overlapping data subsets withheld from the training set. Ddata analysisObtaining an understanding of data by considering samples, measurement, and visualization. Data analysis can be particularly useful when a dataset is first received, before one builds the first model. It is also crucial in understanding experiments and debugging problems with the system. data augmentationArtificially boosting the range and number of training examples by transforming existing examples to create additional examples. For example, suppose images are one of your features, but your dataset doesn't contain enough image examples for the model to learn useful associations. Ideally, you'd add enough labeled images to your dataset to enable your model to train properly. If that's not possible, data augmentation can rotate, stretch, and reflect each image to produce many variants of the original picture, possibly yielding enough labeled data to enable excellent training. DataFrameA popular pandas datatype for representing datasets in memory. A DataFrame is analogous to a table or a spreadsheet. Each column of a DataFrame has a name (a header), and each row is identified by a unique number. Each column in a DataFrame is structured like a 2D array, except that each column can be assigned its own data type. See also the official pandas.DataFrame reference page. data parallelismA way of scaling training or inference that replicates an entire model onto multiple devices and then passes a subset of the input data to each device. Data parallelism can enable training and inference on very large batch sizes; however, data parallelism requires that the model be small enough to fit on all devices. See also model parallelism. data set or datasetA collection of raw data, commonly (but not exclusively) organized in one of the following formats:
Dataset API (tf.data)A high-level TensorFlow API for reading data and transforming it into a form that a machine learning algorithm requires. A For details about the Dataset API, see tf.data: Build TensorFlow input pipelines in the TensorFlow Programmer's Guide. decision boundaryThe separator between classes learned by a model in a binary class or multi-class classification problems. For example, in the following image representing a binary classification problem, the decision boundary is the frontier between the orange class and the blue class:
decision forestA model created from multiple decision trees. A decision forest makes a prediction by aggregating the predictions of its decision trees. Popular types of decision forests include random forests and gradient boosted trees. decision thresholdSynonym for classification threshold. decision treeA supervised learning model composed of a set of conditions and leaves organized hierarchically. For example, the following is a decision tree:
deep modelA neural network containing more than one hidden layer. A deep model is also called a deep neural network. Contrast with wide model. decoderIn general, any ML system that converts from a processed, dense, or internal representation to a more raw, sparse, or external representation. Decoders are often a component of a larger model, where they are frequently paired with an encoder. In sequence-to-sequence tasks, a decoder starts with the internal state generated by the encoder to predict the next sequence. Refer to Transformer for the definition of a decoder within the Transformer architecture. deep neural networkSynonym for deep model. Deep Q-Network (DQN)In Q-learning, a deep neural network that predicts Q-functions. Critic is a synonym for Deep Q-Network. demographic parityA fairness metric that is satisfied if the results of a model's classification are not dependent on a given sensitive attribute. For example, if both Lilliputians and Brobdingnagians apply to Glubbdubdrib University, demographic parity is achieved if the percentage of Lilliputians admitted is the same as the percentage of Brobdingnagians admitted, irrespective of whether one group is on average more qualified than the other. Contrast with equalized odds and equality of opportunity, which permit classification results in aggregate to depend on sensitive attributes, but do not permit classification results for certain specified ground-truth labels to depend on sensitive attributes. See "Attacking discrimination with smarter machine learning" for a visualization exploring the tradeoffs when optimizing for demographic parity. denoisingA common approach to self-supervised learning in which:
Denoising enables learning from unlabeled examples. The original dataset serves as the target or label and the noisy data as the input. Some masked language models use denoising as follows:
dense featureA feature in which most or all values are nonzero, typically a Tensor of floating-point values. For example, the following 10-element Tensor is dense because 9 of its values are nonzero:
Contrast with sparse feature. dense layerSynonym for fully connected layer. depthThe sum of the following in a neural network:
For example, a neural network with five hidden layers and one output layer has a depth of 6. Notice that the input layer does not influence depth. depthwise separable convolutional neural network (sepCNN)A convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions. Also known as Xception. A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n ✕ n ✕ 1), and then second, a pointwise convolution, with length and width of 1 (1 ✕ 1 ✕ n). To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions. derived labelSynonym for proxy label. deviceA category of hardware that can run a TensorFlow session, including CPUs, GPUs, and TPUs. dimension reductionDecreasing the number of dimensions used to represent a particular feature in a feature vector, typically by converting to an embedding vector. dimensionsOverloaded term having any of the following definitions:
discrete featureA feature with a finite set of possible values. For example, a feature whose values may only be animal, vegetable, or mineral is a discrete (or categorical) feature. Contrast with continuous feature. discriminative modelA model that predicts labels from a set of one or more features. More formally, discriminative models define the conditional probability of an output given the features and weights; that is: p(output | features, weights) For example, a model that predicts whether an email is spam from features and weights is a discriminative model. The vast majority of supervised learning models, including classification and regression models, are discriminative models. Contrast with generative model. discriminatorA system that determines whether examples are real or fake. Alternatively, the subsystem within a generative adversarial network that determines whether the examples created by the generator are real or fake. disparate impactMaking decisions about people that impact different population subgroups disproportionately. This usually refers to situations where an algorithmic decision-making process harms or benefits some subgroups more than others. For example, suppose an algorithm that determines a Lilliputian's eligibility for a miniature-home loan is more likely to classify them as “ineligible” if their mailing address contains a certain postal code. If Big-Endian Lilliputians are more likely to have mailing addresses with this postal code than Little-Endian Lilliputians, then this algorithm may result in disparate impact. Contrast with disparate treatment, which focuses on disparities that result when subgroup characteristics are explicit inputs to an algorithmic decision-making process. disparate treatmentFactoring subjects' sensitive attributes into an algorithmic decision-making process such that different subgroups of people are treated differently. For example, consider an algorithm that determines Lilliputians’ eligibility for a miniature-home loan based on the data they provide in their loan application. If the algorithm uses a Lilliputian’s affiliation as Big-Endian or Little-Endian as an input, it is enacting disparate treatment along that dimension. Contrast with disparate impact, which focuses on disparities in the societal impacts of algorithmic decisions on subgroups, irrespective of whether those subgroups are inputs to the model. divisive clusteringSee hierarchical clustering. downsamplingOverloaded term that can mean either of the following:
DQNAbbreviation for Deep Q-Network. dropout regularizationA form of regularization useful in training neural networks. Dropout regularization removes a random selection of a fixed number of the units in a network layer for a single gradient step. The more units dropped out, the stronger the regularization. This is analogous to training the network to emulate an exponentially large ensemble of smaller networks. For full details, see Dropout: A Simple Way to Prevent Neural Networks from Overfitting. dynamicSomething done frequently or continuously. The terms dynamic and online are synonyms in machine learning. The following are common uses of dynamic and online in machine learning:
dynamic modelA model that is frequently (maybe even continuously) retrained. A dynamic model is a "lifelong learner" that constantly adapts to evolving data. A dynamic model is also known as an online model. Contrast with static model. Eeager executionA TensorFlow programming environment in which operations run immediately. In contrast, operations called in graph execution don't run until they are explicitly evaluated. Eager execution is an imperative interface, much like the code in most programming languages. Eager execution programs are generally far easier to debug than graph execution programs. early stoppingA method for regularization that involves ending training before training loss finishes decreasing. In early stopping, you intentionally stop training the model when the loss on a validation dataset starts to increase; that is, when generalization performance worsens. Click the icon for additional notes.Early stopping may seem counterintuitive. After all, telling a model to halt training while the loss is still decreasing may seem like telling a chef to stop cooking before the dessert has fully baked. However, training a model for too long can lead to overfitting. That is, if you train a model too long, the model may fit the training data so closely that the model doesn't make good predictions on new examples. earth mover's distance (EMD)A measure of the relative similarity between two documents. The lower the earth mover's distance, the more similar the documents. embedding layerA special hidden layer that trains on a high-dimensional categorical feature to gradually learn a lower dimension embedding vector. An embedding layer enables a neural network to train far more efficiently than training just on the high-dimensional categorical feature. For example, Earth currently supports about 73,000 tree species. Suppose tree species is a
feature in your model, so your model's input layer includes a one-hot vector 73,000 elements long. For example, perhaps
A 73,000-element array is very long. If you don't add an embedding layer to the model, training is going to be very time consuming due to multiplying 72,999 zeros. Perhaps you pick the embedding layer to consist of 12 dimensions. Consequently, the embedding layer will gradually learn a new embedding vector for each tree species. In certain situations, hashing is a reasonable alternative to an embedding layer. embedding spaceThe d-dimensional vector space that features from a higher-dimensional vector space are mapped to. Ideally, the embedding space contains a structure that yields meaningful mathematical results; for example, in an ideal embedding space, addition and subtraction of embeddings can solve word analogy tasks. The dot product of two embeddings is a measure of their similarity. embedding vectorBroadly speaking, an array of floating-point numbers taken from any hidden layer that describe the inputs to that hidden layer. Often, an embedding vector is the array of floating-point numbers trained in an embedding layer. For example, suppose an embedding layer must learn an embedding vector for each of the 73,000 tree species on Earth. Perhaps the following array is the embedding vector for a baobab tree:
An embedding vector is not a bunch of random numbers. An embedding layer determines these values through training, similar to the way a neural network learns other weights during training. Each element of the array is a rating along some characteristic of a tree species. Which element represents which tree species' characteristic? That's very hard for humans to determine. The mathematically remarkable part of an embedding vector is that similar items have similar sets of floating-point numbers. For example, similar tree species have a more similar set of floating-point numbers than dissimilar tree species. Redwoods and sequoias are related tree species, so they'll have a more similar set of floating-pointing numbers than redwoods and coconut palms. The numbers in the embedding vector will change each time you retrain the model, even if you retrain the model with identical input. empirical risk minimization (ERM)Choosing the function that minimizes loss on the training set. Contrast with structural risk minimization. encoderIn general, any ML system that converts from a raw, sparse, or external representation into a more processed, denser, or more internal representation. Encoders are often a component of a larger model, where they are frequently paired with a decoder. Some Transformers pair encoders with decoders, though other Transformers use only the encoder or only the decoder. Some systems use the encoder's output as the input to a classification or regression network. In sequence-to-sequence tasks, an encoder takes an input sequence and returns an internal state (a vector). Then, the decoder uses that internal state to predict the next sequence. Refer to Transformer for the definition of an encoder in the Transformer architecture. ensembleA collection of models trained independently whose predictions are averaged or aggregated. In many cases, an ensemble produces better predictions than a single model. For example, a random forest is an ensemble built from multiple decision trees. Note that not all decision forests are ensembles. entropyIn information theory, a description of how unpredictable a probability distribution is. Alternatively, entropy is also defined as how much information each example contains. A distribution has the highest possible entropy when all values of a random variable are equally likely. The entropy of a set with two possible values "0" and "1" (for example, the labels in a binary classification problem) has the following formula: H = -p log p - q log q = -p log p - (1-p) * log (1-p) where:
For example, suppose the following:
Therefore, the entropy value is:
A set that is perfectly balanced (for example, 200 "0"s and 200 "1"s) would have an entropy of 1.0 bit per example. As a set becomes more imbalanced, its entropy moves towards 0.0. In decision trees, entropy helps formulate information gain to help the splitter select the conditions during the growth of a classification decision tree. Compare entropy with:
Entropy is often called Shannon's entropy. environmentIn reinforcement learning, the world that contains the agent and allows the agent to observe that world's state. For example, the represented world can be a game like chess, or a physical world like a maze. When the agent applies an action to the environment, then the environment transitions between states. episodeIn reinforcement learning, each of the repeated attempts by the agent to learn an environment. epochA full training pass over the entire training set such that each example has been processed once. An epoch represents For instance, suppose the following:
Therefore, a single epoch requires 20 iterations: 1 epoch = (N/batch size) = (1,000 / 50) = 20 iterations epsilon greedy policyIn reinforcement learning, a policy that either follows a random policy with epsilon probability or a greedy policy otherwise. For example, if epsilon is 0.9, then the policy follows a random policy 90% of the time and a greedy policy 10% of the time. Over successive episodes, the algorithm reduces epsilon’s value in order to shift from following a random policy to following a greedy policy. By shifting the policy, the agent first randomly explores the environment and then greedily exploits the results of random exploration. equality of opportunityA fairness metric that checks whether, for a preferred label (one that confers an advantage or benefit to a person) and a given attribute, a classifier predicts that preferred label equally well for all values of that attribute. In other words, equality of opportunity measures whether the people who should qualify for an opportunity are equally likely to do so regardless of their group membership. For example, suppose Glubbdubdrib University admits both Lilliputians and Brobdingnagians to a rigorous mathematics program. Lilliputians’ secondary schools offer a robust curriculum of math classes, and the vast majority of students are qualified for the university program. Brobdingnagians’ secondary schools don’t offer math classes at all, and as a result, far fewer of their students are qualified. Equality of opportunity is satisfied for the preferred label of "admitted" with respect to nationality (Lilliputian or Brobdingnagian) if qualified students are equally likely to be admitted irrespective of whether they're a Lilliputian or a Brobdingnagian. For example, let's say 100 Lilliputians and 100 Brobdingnagians apply to Glubbdubdrib University, and admissions decisions are made as follows: Table 1. Lilliputian applicants (90% are qualified)
Table 2. Brobdingnagian applicants (10% are qualified):
The preceding examples satisfy equality of opportunity for acceptance of qualified students because qualified Lilliputians and Brobdingnagians both have a 50% chance of being admitted. See "Equality of Opportunity in Supervised Learning" for a more detailed discussion of equality of opportunity. Also see "Attacking discrimination with smarter machine learning" for a visualization exploring the tradeoffs when optimizing for equality of opportunity. equalized oddsA fairness metric that checks if, for any particular label and attribute, a classifier predicts that label equally well for all values of that attribute. For example, suppose Glubbdubdrib University admits both Lilliputians and Brobdingnagians to a rigorous mathematics program. Lilliputians' secondary schools offer a robust curriculum of math classes, and the vast majority of students are qualified for the university program. Brobdingnagians' secondary schools don’t offer math classes at all, and as a result, far fewer of their students are qualified. Equalized odds is satisfied provided that no matter whether an applicant is a Lilliputian or a Brobdingnagian, if they are qualified, they are equally as likely to get admitted to the program, and if they are not qualified, they are equally as likely to get rejected. Let’s say 100 Lilliputians and 100 Brobdingnagians apply to Glubbdubdrib University, and admissions decisions are made as follows: Table 3. Lilliputian applicants (90% are qualified)
Table 4. Brobdingnagian applicants (10% are qualified):
Equalized odds is satisfied because qualified Lilliputian and Brobdingnagian students both have a 50% chance of being admitted, and unqualified Lilliputian and Brobdingnagian have an 80% chance of being rejected. Equalized odds is formally defined in "Equality of Opportunity in Supervised Learning" as follows: "predictor Ŷ satisfies equalized odds with respect to protected attribute A and outcome Y if Ŷ and A are independent, conditional on Y." EstimatorA deprecated TensorFlow API. Use tf.keras instead of Estimators. exampleThe values of one row of features and possibly a label. Examples in supervised learning fall into two general categories:
For instance, suppose you are training a model to determine the influence of weather conditions on student test scores. Here are three labeled examples:
Here are three unlabeled examples:
The row of a dataset is typically the raw source for an example. That is, an example typically consists of a subset of the columns in the dataset. Furthermore, the features in an example can also include synthetic features, such as feature crosses. experience replayIn reinforcement learning, a DQN technique used to reduce temporal correlations in training data. The agent stores state transitions in a replay buffer, and then samples transitions from the replay buffer to create training data. experimenter's biasSee confirmation bias. exploding gradient problemThe tendency for gradients in deep neural networks (especially recurrent neural networks) to become surprisingly steep (high). Steep gradients often cause very large updates to the weights of each node in a deep neural network. Models suffering from the exploding gradient problem become difficult or impossible to train. Gradient clipping can mitigate this problem. Compare to vanishing gradient problem. Ffairness constraintApplying a constraint to an algorithm to ensure one or more definitions of fairness are satisfied. Examples of fairness constraints include:
fairness metricA mathematical definition of “fairness” that is measurable. Some commonly used fairness metrics include:
Many fairness metrics are mutually exclusive; see incompatibility of fairness metrics. false negative (FN)An example in which the model mistakenly predicts the negative class. For example, the model predicts that a particular email message is not spam (the negative class), but that email message actually is spam. false negative rateThe proportion of actual positive examples for which the model mistakenly predicted the negative class. The following formula calculates the false negative rate: $$\text{false negative rate} = \frac{\text{false negatives}}{\text{false negatives} + \text{true positives}}$$ false positive (FP)An example in which the model mistakenly predicts the positive class. For example, the model predicts that a particular email message is spam (the positive class), but that email message is actually not spam. false positive rate (FPR)The proportion of actual negative examples for which the model mistakenly predicted the positive class. The following formula calculates the false positive rate: $$\text{false positive rate} = \frac{\text{false positives}}{\text{false positives} + \text{true negatives}}$$ The false positive rate is the x-axis in an ROC curve. featureAn input variable to a machine learning model. An example consists of one or more features. For instance, suppose you are training a model to determine the influence of weather conditions on student test scores. The following table shows three examples, each of which contains three features and one label:
Contrast with label. feature crossA synthetic feature formed by "crossing" categorical or bucketed features. For example, consider a "mood forecasting" model that represents temperature in one of the following four buckets:
And represents wind speed in one of the following three buckets:
Without feature crosses, the linear model trains independently on each of the preceding seven various buckets. So, the model trains on, for instance, Alternatively, you could create a feature cross of temperature and wind speed. This synthetic feature would have the following 12 possible values:
Thanks to feature crosses, the model can learn mood differences between a If you create a synthetic feature from two features that each have a lot of different buckets, the resulting feature cross will have a huge number of possible combinations. For example, if one feature has 1,000 buckets and the other feature has 2,000 buckets, the resulting feature cross has 2,000,000 buckets. Formally, a cross is a Cartesian product. Feature crosses are mostly used with linear models and are rarely used with neural networks. feature engineering#fundamentals #TensorFlow A process that involves the following steps:
For example, you might determine that Feature engineering is sometimes called feature extraction. Click the icon for additional notes about TensorFlow.In TensorFlow, feature engineering often means converting raw log file entries to tf.Example protocol buffers. See also tf.Transform. Overloaded term having either of the following definitions:
feature importancesSynonym for variable importances. feature setThe group of features your machine learning model trains on. For example, postal code, property size, and property condition might comprise a simple feature set for a model that predicts housing prices. feature specDescribes the information required to extract features data from the tf.Example protocol buffer. Because the tf.Example protocol buffer is just a container for data, you must specify the following:
feature vectorThe array of feature values comprising an example. The feature vector is input during training and during inference. For example, the feature vector for a model with two discrete features might be: [0.92, 0.56]
Each example supplies different values for the feature vector, so the feature vector for the next example could be something like: [0.73, 0.49] Feature engineering determines how to represent features in the feature vector. For example, a binary categorical feature with five possible values might be represented with one-hot encoding. In this case, the portion of the feature vector for a particular example would consist of four zeroes and a single 1.0 in the third position, as follows: [0.0, 0.0, 1.0, 0.0, 0.0] As another example, suppose your model consists of three features:
In this case, the feature vector for each example would be represented by nine values. Given the example values in the preceding list, the feature vector would be: 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 8.3 federated learningA distributed machine learning approach that trains machine learning models using decentralized examples residing on devices such as smartphones. In federated learning, a subset of devices downloads the current model from a central coordinating server. The devices use the examples stored on the devices to make improvements to the model. The devices then upload the model improvements (but not the training examples) to the coordinating server, where they are aggregated with other updates to yield an improved global model. After the aggregation, the model updates computed by devices are no longer needed, and can be discarded. Since the training examples are never uploaded, federated learning follows the privacy principles of focused data collection and data minimization. For more information about federated learning, see this tutorial. feedback loopIn machine learning, a situation in which a model's predictions influence the training data for the same model or another model. For example, a model that recommends movies will influence the movies that people see, which will then influence subsequent movie recommendation models. feedforward neural network (FFN)A neural network without cyclic or recursive connections. For example, traditional deep neural networks are feedforward neural networks. Contrast with recurrent neural networks, which are cyclic. few-shot learningA machine learning approach, often used for object classification, designed to train effective classifiers from only a small number of training examples. See also one-shot learning. fine tuningPerforming a secondary optimization to adjust the parameters of an already trained model to fit a new problem. Fine tuning often refers to refitting the weights of a trained unsupervised model to a supervised model. forget gateThe portion of a Long Short-Term Memory cell that regulates the flow of information through the cell. Forget gates maintain context by deciding which information to discard from the cell state. full softmaxSynonym for softmax. Contrast with candidate sampling. fully connected layerA hidden layer in which each node is connected to every node in the subsequent hidden layer. A fully connected layer is also known as a dense layer. GGANAbbreviation for generative adversarial network. generalizationA model's ability to make correct predictions on new, previously unseen data. A model that can generalize is the opposite of a model that is overfitting. Click the icon for additional notes.You train a model on the examples in the training set. Consequently, the model learns the peculiarities of the data in the training set. Generalization essentially asks whether your model can make good predictions on examples that are not in the training set. To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data in the training set. generalization curveA plot of both training loss and validation loss as a function of the number of iterations. A generalization curve can help you detect possible overfitting. For example, the following generalization curve suggests overfitting because validation loss ultimately becomes significantly higher than training loss.
generalized linear modelA generalization of least squares regression models, which are based on Gaussian noise, to other types of models based on other types of noise, such as Poisson noise or categorical noise. Examples of generalized linear models include:
The parameters of a generalized linear model can be found through convex optimization. Generalized linear models exhibit the following properties:
The power of a generalized linear model is limited by its features. Unlike a deep model, a generalized linear model cannot "learn new features." generative adversarial network (GAN)A system to create new data in which a generator creates data and a discriminator determines whether that created data is valid or invalid. generative modelPractically speaking, a model that does either of the following:
A generative model can theoretically discern the distribution of examples or particular features in a dataset. That is: p(examples) Unsupervised learning models are generative. Contrast with discriminative models. generatorThe subsystem within a generative adversarial network that creates new examples. Contrast with discriminative model. GPT (Generative Pre-trained Transformer)A family of Transformer-based large language models developed by OpenAI. GPT variants can apply to multiple modalities, including:
gini impurityA metric similar to entropy. Splitters use values derived from either gini impurity or entropy to compose conditions for classification decision trees. Information gain is derived from entropy. There is no universally accepted equivalent term for the metric derived from gini impurity; however, this unnamed metric is just as important as information gain. Gini impurity is also called gini index, or simply gini. Click the icon for mathematical details about gini impurity.Gini impurity is the probability of misclassifying a new piece of data taken from the same distribution. The gini impurity of a set with two possible values "0" and "1" (for example, the labels in a binary classification problem) is calculated from the following formula: I = 1 - (p2 + q2) = 1 - (p2 + (1-p)2) where:
For example, consider the following dataset:
Therefore, the gini impurity is:
Consequently, a random label from the same dataset would have a 37.5% chance of being misclassified, and a 62.5% chance of being properly classified. A perfectly balanced label (for example, 200 "0"s and 200 "1"s) would have a gini impurity of 0.5. A highly imbalanced label would have a gini impurity close to 0.0. gradientThe vector of partial derivatives with respect to all of the independent variables. In machine learning, the gradient is the vector of partial derivatives of the model function. The gradient points in the direction of steepest ascent. gradient boostingA training algorithm where weak models are trained to iteratively improve the quality (reduce the loss) of a strong model. For example, a weak model could be a linear or small decision tree model. The strong model becomes the sum of all the previously trained weak models. In the simplest form of gradient boosting, at each iteration, a weak model is trained to predict the loss gradient of the strong model. Then, the strong model's output is updated by subtracting the predicted gradient, similar to gradient descent. $$F_{0} = 0$$ $$F_{i+1} = F_i - \xi f_i $$ where:
Modern variations of gradient boosting also include the second derivative (Hessian) of the loss in their computation. Decision trees are commonly used as weak models in gradient boosting. See gradient boosted (decision) trees. gradient boosted (decision) trees (GBT)A type of decision forest in which:
gradient clippingA commonly used mechanism to mitigate the exploding gradient problem by artificially limiting (clipping) the maximum value of gradients when using gradient descent to train a model. gradient descentA mathematical technique to minimize loss. Gradient descent iteratively adjusts weights and biases, gradually finding the best combination to minimize loss. Gradient descent is older—much, much older—than machine learning. graphIn TensorFlow, a computation specification. Nodes in the graph represent operations. Edges are directed and represent passing the result of an operation (a Tensor) as an operand to another operation. Use TensorBoard to visualize a graph. graph executionA TensorFlow programming environment in which the program first constructs a graph and then executes all or part of that graph. Graph execution is the default execution mode in TensorFlow 1.x. Contrast with eager execution. greedy policyIn reinforcement learning, a policy that always chooses the action with the highest expected return. ground truthReality. The thing that actually happened. For example, consider a binary classification model that predicts whether a student in their first year of university will graduate within six years. Ground truth for this model is whether or not that student actually graduated within six years. Click the icon for additional notes.We assess model quality against ground truth. However, ground truth is not always completely, well, truthful. For example, consider the following examples of potential imperfections in ground truth:
group attribution biasAssuming that what is true for an individual is also true for everyone in that group. The effects of group attribution bias can be exacerbated if a convenience sampling is used for data collection. In a non-representative sample, attributions may be made that do not reflect reality. See also out-group homogeneity bias and in-group bias. HhallucinationThe production of plausible-seeming but factually incorrect output by a generative model that purports to be making an assertion about the real world. For example, if a dialog agent claims that Barack Obama died in 1865, the agent is hallucinating. hashingIn machine learning, a mechanism for bucketing categorical data, particularly when the number of categories is large, but the number of categories actually appearing in the dataset is comparatively small. For example, Earth is home to about 73,000 tree species. You could represent each of the 73,000 tree species in 73,000 separate categorical buckets. Alternatively, if only 200 of those tree species actually appear in a dataset, you could use hashing to divide tree species into perhaps 500 buckets. A single bucket could contain multiple tree species. For example, hashing could place baobab and red maple—two genetically dissimilar species—into the same bucket. Regardless, hashing is still a good way to map large categorical sets into the desired number of buckets. Hashing turns a categorical feature having a large number of possible values into a much smaller number of values by grouping values in a deterministic way. heuristicA simple and quickly implemented solution to a problem. For example, "With a heuristic, we achieved 86% accuracy. When we switched to a deep neural network, accuracy went up to 98%." A layer in a neural network between the input layer (the features) and the output layer (the prediction). Each hidden layer consists of one or more neurons. For example, the following neural network contains two hidden layers, the first with three neurons and the second with two neurons:
A deep neural network contains more than one hidden layer. For example, the preceding illustration is a deep neural network because the model contains two hidden layers. hierarchical clusteringA category of clustering algorithms that create a tree of clusters. Hierarchical clustering is well-suited to hierarchical data, such as botanical taxonomies. There are two types of hierarchical clustering algorithms:
Contrast with centroid-based clustering. hinge lossA family of loss functions for classification designed to find the decision boundary as distant as possible from each training example, thus maximizing the margin between examples and the boundary. KSVMs use hinge loss (or a related function, such as squared hinge loss). For binary classification, the hinge loss function is defined as follows: $$\text{loss} = \text{max}(0, 1 - (y * y'))$$ where y is the true label, either -1 or +1, and y' is the raw output of the classifier model: $$y' = b + w_1x_1 + w_2x_2 + … w_nx_n$$ Consequently, a plot of hinge loss vs. (y * y') looks as follows:
holdout dataExamples intentionally not used ("held out") during training. The validation dataset and test dataset are examples of holdout data. Holdout data helps evaluate your model's ability to generalize to data other than the data it was trained on. The loss on the holdout set provides a better estimate of the loss on an unseen dataset than does the loss on the training set. hyperparameterThe variables that you or a hyperparameter tuning service adjust during successive runs of training a model. For example, learning rate is a hyperparameter. You could set the learning rate to 0.01 before one training session. If you determine that 0.01 is too high, you could perhaps set the learning rate to 0.003 for the next training session. In contrast, parameters are the various weights and bias that the model learns during training. hyperplaneA boundary that separates a space into two subspaces. For example, a line is a hyperplane in two dimensions and a plane is a hyperplane in three dimensions. More typically in machine learning, a hyperplane is the boundary separating a high-dimensional space. Kernel Support Vector Machines use hyperplanes to separate positive classes from negative classes, often in a very high-dimensional space. Ii.i.d.Abbreviation for independently and identically distributed. image recognitionA process that classifies object(s), pattern(s), or concept(s) in an image. Image recognition is also known as image classification. For more information, see ML Practicum: Image Classification. imbalanced datasetSynonym for class-imbalanced dataset. implicit biasAutomatically making an association or assumption based on one’s mental models and memories. Implicit bias can affect the following:
For example, when building a classifier to identify wedding photos, an engineer may use the presence of a white dress in a photo as a feature. However, white dresses have been customary only during certain eras and in certain cultures. See also confirmation bias. incompatibility of fairness metricsThe idea that some notions of fairness are mutually incompatible and cannot be satisfied simultaneously. As a result, there is no single universal metric for quantifying fairness that can be applied to all ML problems. While this may seem discouraging, incompatibility of fairness metrics doesn’t imply that fairness efforts are fruitless. Instead, it suggests that fairness must be defined contextually for a given ML problem, with the goal of preventing harms specific to its use cases. See "On the (im)possibility of fairness" for a more detailed discussion of this topic. independently and identically distributed (i.i.d)Data drawn from a distribution that doesn't change, and where each value drawn doesn't depend on values that have been drawn previously. An i.i.d. is the ideal gas of machine learning—a useful mathematical construct but almost never exactly found in the real world. For example, the distribution of visitors to a web page may be i.i.d. over a brief window of time; that is, the distribution doesn't change during that brief window and one person's visit is generally independent of another's visit. However, if you expand that window of time, seasonal differences in the web page's visitors may appear. See also nonstationarity. individual fairnessA fairness metric that checks whether similar individuals are classified similarly. For example, Brobdingnagian Academy might want to satisfy individual fairness by ensuring that two students with identical grades and standardized test scores are equally likely to gain admission. Note that individual fairness relies entirely on how you define "similarity" (in this case, grades and test scores), and you can run the risk of introducing new fairness problems if your similarity metric misses important information (such as the rigor of a student’s curriculum). See "Fairness Through Awareness" for a more detailed discussion of individual fairness. inferenceIn machine learning, the process of making predictions by applying a trained model to unlabeled examples. Inference has a somewhat different meaning in statistics. See the Wikipedia article on statistical inference for details. inference pathIn a decision tree, during inference, the route a particular example takes from the root to other conditions, terminating with a leaf. For instance, in the following decision tree, the thicker arrows show the inference path for an example with the following feature values:
The inference path in the following illustration travels through three conditions before reaching the leaf (
The three thick arrows show the inference path. information gainIn decision forests, the difference between a node's entropy and the weighted (by number of examples) sum of the entropy of its children nodes. A node's entropy is the entropy of the examples in that node. For example, consider the following entropy values:
So 40% of the examples are in one child node and 60% are in the other child node. Therefore:
So, the information gain is:
Most splitters seek to create conditions that maximize information gain. in-group biasShowing partiality to one's own group or own characteristics. If testers or raters consist of the machine learning developer's friends, family, or colleagues, then in-group bias may invalidate product testing or the dataset. In-group bias is a form of group attribution bias. See also out-group homogeneity bias. input layerThe layer of a neural network that holds the feature vector. That is, the input layer provides examples for training or inference. For example, the input layer in the following neural network consists of two features:
in-set conditionIn a decision tree, a condition that tests for the presence of one item in a set of items. For example, the following is an in-set condition:
During inference, if the value of the house-style feature is In-set conditions usually lead to more efficient decision trees than conditions that test one-hot encoded features. instanceSynonym for example. interpretabilityThe ability to explain or to present an ML model's reasoning in understandable terms to a human. Most linear regression models, for example, are highly interpretable. (You merely need to look at the trained weights for each feature.) Decision forests are also highly interpretable. Some models, however, require sophisticated visualization to become interpretable. inter-rater agreementA measurement of how often human raters agree when doing a task. If raters disagree, the task instructions may need to be improved. Also sometimes called inter-annotator agreement or inter-rater reliability. See also Cohen's kappa, which is one of the most popular inter-rater agreement measurements. intersection over union (IoU)The intersection of two sets divided by their union. In machine-learning image-detection tasks, IoU is used to measure the accuracy of the model’s predicted bounding box with respect to the ground-truth bounding box. In this case, the IoU for the two boxes is the ratio between the overlapping area and the total area, and its value ranges from 0 (no overlap of predicted bounding box and ground-truth bounding box) to 1 (predicted bounding box and ground-truth bounding box have the exact same coordinates). For example, in the image below:
Here, the intersection of the bounding boxes for prediction and ground truth (below left) is 1, and the union of the bounding boxes for prediction and ground truth (below right) is 7, so the IoU is \(\frac{1}{7}\). IoUAbbreviation for intersection over union. item matrixIn recommendation systems, a matrix of embedding vectors generated by matrix factorization that holds latent signals about each item. Each row of the item matrix holds the value of a single latent feature for all items. For example, consider a movie recommendation system. Each column in the item matrix represents a single movie. The latent signals might represent genres, or might be harder-to-interpret signals that involve complex interactions among genre, stars, movie age, or other factors. The item matrix has the same number of columns as the target matrix that is being factorized. For example, given a movie recommendation system that evaluates 10,000 movie titles, the item matrix will have 10,000 columns. itemsIn a recommendation system, the entities that a system recommends. For example, videos are the items that a video store recommends, while books are the items that a bookstore recommends. iterationA single update of a model's parameters—the model's weights and biases—during training. The batch size determines how many examples the model processes in a single iteration. For instance, if the batch size is 20, then the model processes 20 examples before adjusting the parameters. When training a neural network, a single iteration involves the following two passes:
KKerasA popular Python machine learning API. Keras runs on several deep learning frameworks, including TensorFlow, where it is made available as tf.keras. keypointsThe coordinates of particular features in an image. For example, for an image recognition model that distinguishes flower species, keypoints might be the center of each petal, the stem, the stamen, and so on. Kernel Support Vector Machines (KSVMs)A classification algorithm that seeks to maximize the margin between positive and negative classes by mapping input data vectors to a higher dimensional space. For example, consider a classification problem in which the input dataset has a hundred features. To maximize the margin between positive and negative classes, a KSVM could internally map those features into a million-dimension space. KSVMs uses a loss function called hinge loss. k-meansA popular clustering algorithm that groups examples in unsupervised learning. The k-means algorithm basically does the following:
The k-means algorithm picks centroid locations to minimize the cumulative square of the distances from each example to its closest centroid. For example, consider the following plot of dog height to dog width:
If k=3, the k-means algorithm will determine three centroids. Each example is assigned to its closest centroid, yielding three groups:
Imagine that a manufacturer wants to determine the ideal sizes for small, medium, and large sweaters for dogs. The three centroids identify the mean height and mean width of each dog in that cluster. So, the manufacturer should probably base sweater sizes on those three centroids. Note that the centroid of a cluster is typically not an example in the cluster. The preceding illustrations shows k-means for examples with only two features (height and width). Note that k-means can group examples across many features. A clustering algorithm closely related to k-means. The practical difference between the two is as follows:
Note that the definitions of distance are also different:
$$ {\text{Euclidean distance}} = {\sqrt {(2-5)^2 + (2--2)^2}} = 5 $$
$$ {\text{Manhattan distance}} = \lvert 2-5 \rvert + \lvert 2--2 \rvert = 7 $$ LL0 regularizationA type of regularization that penalizes the total number of nonzero weights in a model. For example, a model having 11 nonzero weights would be penalized more than a similar model having 10 nonzero weights. L0 regularization is seldom used. Click the icon for additional notes.L1 lossA loss function that calculates the absolute value of the difference between actual label values and the values that a model predicts. For example, here's the calculation of L1 loss for a batch of five examples:
L1 loss is less sensitive to outliers than L2 loss. The Mean Absolute Error is the average L1 loss per example. Click the icon to see the formal math.$$ L_1 loss = \sum_{i=0}^n | y_i - \hat{y}_i |$$ where:
L1 regularizationA type of regularization that penalizes weights in proportion to the sum of the absolute value of the weights. L1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0. A feature with a weight of 0 is effectively removed from the model. Contrast with L2 regularization. L2 lossA loss function that calculates the square of the difference between actual label values and the values that a model predicts. For example, here's the calculation of L2 loss for a batch of five examples:
Due to squaring, L2 loss amplifies the influence of outliers. That is, L2 loss reacts more strongly to bad predictions than L1 loss. For example, the L1 loss for the preceding batch would be 8 rather than 16. Notice that a single outlier accounts for 9 of the 16. Regression models typically use L2 loss as the loss function. The Mean Squared Error is the average L2 loss per example. Squared loss is another name for L2 loss. Click the icon to see the formal math.$$ L_2 loss = \sum_{i=0}^n {(y_i - \hat{y}_i)}^2$$ where:
L2 regularizationA type of regularization that penalizes weights in proportion to the sum of the squares of the weights. L2 regularization helps drive outlier weights (those with high positive or low negative values) closer to 0 but not quite to 0. Features with values very close to 0 remain in the model but don't influence the model's prediction very much. L2 regularization always improves generalization in linear models. Contrast with L1 regularization. labelIn supervised machine learning, the "answer" or "result" portion of an example. Each labeled example consists of one or more features and a label. For instance, in a spam detection dataset, the label would probably be either "spam" or "not spam." In a rainfall dataset, the label might be the amount of rain that fell during a certain period. labeled exampleAn example that contains one or more features and a label. For example, the following table shows three labeled examples from a house valuation model, each with three features and one label:
In supervised machine learning, models train on labeled examples and make predictions on unlabeled examples. Contrast labeled example with unlabeled examples. LaMDA (Language Model for Dialogue Applications)A Transformer-based large language model developed by Google trained on a large dialogue dataset that can generate realistic conversational responses. LaMDA: our breakthrough conversation technology provides an overview. lambdaSynonym for regularization rate. Lambda is an overloaded term. Here we're focusing on the term's definition within regularization. landmarksSynonym for keypoints. language modelA model that estimates the probability of a token or sequence of tokens occurring in a longer sequence of tokens. Click the icon for additional notes.Though counterintuitive, many models that evaluate text are not language models. For example, text classification models and sentiment analysis models are not language models. large language modelAn informal term with no strict definition that usually means a language model that has a high number of parameters. Some large language models contain over 100 billion parameters. Click the icon for additional notes.You might be wondering when a language model becomes large enough to be termed a large language model. Currently, there is no agreed-upon defining line for the number of parameters. Most current large language models (for example, GPT) are based on Transformer architecture. layerA set of neurons in a neural network. Three common types of layers are as follows:
For example, the following illustration shows a neural network with one input layer, two hidden layers, and one output layer:
In TensorFlow, layers are also Python functions that take Tensors and configuration options as input and produce other tensors as output. Layers API (tf.layers)A TensorFlow API for constructing a deep neural network as a composition of layers. The Layers API enables you to build different types of layers, such as:
The Layers API follows the Keras layers API conventions. That is, aside from a different prefix, all functions in the Layers API have the same names and signatures as their counterparts in the Keras layers API. leafAny endpoint in a decision tree. Unlike a condition, a leaf does not perform a test. Rather, a leaf is a possible prediction. A leaf is also the terminal node of an inference path. For example, the following decision tree contains three leaves:
learning rateA floating-point number that tells the gradient descent algorithm how strongly to adjust weights and biases on each iteration. For example, a learning rate of 0.3 would adjust weights and biases three times more powerfully than a learning rate of 0.1. Learning rate is a key hyperparameter. If you set the learning rate too low, training will take too long. If you set the learning rate too high, gradient descent often has trouble reaching convergence. Click the icon for a more mathematical explanation.During each iteration, the gradient descent algorithm multiplies the learning rate by the gradient. The resulting product is called the gradient step. least squares regressionA linear regression model trained by minimizing L2 Loss. linear modelA model that assigns one weight per feature to make predictions. (Linear models also incorporate a bias.) In contrast, the relationship of features to predictions in deep models is generally nonlinear. Linear models are usually easier to train and more interpretable than deep models. However, deep models can learn complex relationships between features. Linear regression and logistic regression are two types of linear models. Click the icon to see the math.A linear model follows this formula: $$y' = b + w_1x_1 + w_2x_2 + … w_nx_n$$ where:
For example, suppose a linear model for three features learns the following bias and weights:
Therefore, given three features (x1, x2, and x3), the linear model uses the following equation to generate each prediction: y' = 7 + (-2.5)(x1) + (-1.2)(x2) + (1.4)(x3) Suppose a particular example contains the following values:
Plugging those values into the formula yields a prediction for this example: y' = 7 + (-2.5)(4) + (-1.2)(-10) + (1.4)(5) y' = 16 Linear models include not only models that use only a linear equation to make predictions but also a broader set of models that use a linear equation as just one component of the formula that makes predictions. For example, logistic regression post-processes the raw prediction (y') to produce a final prediction value between 0 and 1, exclusively. linearA relationship between two or more variables that can be represented solely through addition and multiplication. The plot of a linear relationship is a line. Contrast with nonlinear. linear regressionA type of machine learning model in which both of the following are true:
Contrast linear regression with logistic regression. Also, contrast regression with classification. logistic regressionA type of regression model that predicts a probability. Logistic regression models have the following characteristics:
For example, consider a logistic regression model that calculates the probability of an input email being either spam or not spam. During inference, suppose the model predicts 0.72. Therefore, the model is estimating:
A logistic regression model uses the following two-step architecture:
Like any regression model, a logistic regression model predicts a number. However, this number typically becomes part of a binary classification model as follows:
logitsThe vector of raw (non-normalized) predictions that a classification model generates, which is ordinarily then passed to a normalization function. If the model is solving a multi-class classification problem, logits typically become an input to the softmax function. The softmax function then generates a vector of (normalized) probabilities with one value for each possible class. tf.nn.sigmoid_cross_entropy_with_logits. Log LossThe loss function used in binary logistic regression. Click the icon to see the math.The following formula calculates Log Loss: $$\text{Log Loss} = \sum_{(x,y)\in D} -y\log(y') - (1 - y)\log(1 - y')$$ where:
log-oddsThe logarithm of the odds of some event. Click the icon to see the math.If the event is a binary probability, then odds refers to the ratio of the probability of success (p) to the probability of failure (1-p). For example, suppose that a given event has a 90% probability of success and a 10% probability of failure. In this case, odds is calculated as follows: $$ {\text{odds}} = \frac{\text{p}} {\text{(1-p)}} = \frac{.9} {.1} = {\text{9}} $$ The log-odds is simply the logarithm of the odds. By convention, "logarithm" refers to natural logarithm, but logarithm could actually be any base greater than 1. Sticking to convention, the log-odds of our example is therefore: $$ {\text{log-odds}} = ln(9) ~= 2.2 $$ The log-odds function is the inverse of the sigmoid function. Long Short-Term Memory (LSTM)A type of cell in a recurrent neural network used to process sequences of data in applications such as handwriting recognition, machine translation, and image captioning. LSTMs address the vanishing gradient problem that occurs when training RNNs due to long data sequences by maintaining history in an internal memory state based on new input and context from previous cells in the RNN. lossDuring the training of a supervised model, a measure of how far a model's prediction is from its label. A loss function calculates the loss. loss curveA plot of loss as a function of the number of training iterations. The following plot shows a typical loss curve:
Loss curves can help you determine when your model is converging or overfitting. Loss curves can plot all of the following types of loss:
See also generalization curve. loss functionDuring training or testing, a mathematical function that calculates the loss on a batch of examples. A loss function returns a lower loss for models that makes good predictions than for models that make bad predictions. The goal of training is typically to minimize the loss that a loss function returns. Many different kinds of loss functions exist. Pick the appropriate loss function for the kind of model you are building. For example:
loss surfaceA graph of weight(s) vs. loss. Gradient descent aims to find the weight(s) for which the loss surface is at a local minimum. LSTMAbbreviation for Long Short-Term Memory. Mmachine learningA program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems. majority classThe more common label in a class-imbalanced dataset. For example, given a dataset containing 99% negative labels and 1% positive labels, the negative labels are the majority class. Contrast with minority class. Markov decision process (MDP)A graph representing the decision-making model where decisions (or actions) are taken to navigate a sequence of states under the assumption that the Markov property holds. In reinforcement learning, these transitions between states return a numerical reward. Markov propertyA property of certain environments, where state transitions are entirely determined by information implicit in the current state and the agent’s action. masked language modelA language model that predicts the probability of candidate tokens to fill in blanks in a sequence. For instance, a masked language model can calculate probabilities for candidate word(s) to replace the underline in the following sentence:
The literature typically uses the string "MASK" instead of an underline. For example:
Most modern masked language models are bidirectional. matplotlibAn open-source Python 2D plotting library. matplotlib helps you visualize different aspects of machine learning. matrix factorizationIn math, a mechanism for finding the matrices whose dot product approximates a target matrix. In recommendation systems, the target matrix often holds users' ratings on items. For example, the target matrix for a movie recommendation system might look something like the following, where the positive integers are user ratings and 0 means that the user didn't rate the movie:
The movie recommendation system aims to predict user ratings for unrated movies. For example, will User 1 like Black Panther? One approach for recommendation systems is to use matrix factorization to generate the following two matrices:
For example, using matrix factorization on our three users and five items could yield the following user matrix and item matrix: User Matrix Item Matrix 1.1 2.3 0.9 0.2 1.4 2.0 1.2 0.6 2.0 1.7 1.2 1.2 -0.1 2.1 2.5 0.5 The dot product of the user matrix and item matrix yields a recommendation matrix that contains not only the original user ratings but also predictions for the movies that each user hasn't seen. For example, consider User 1's rating of Casablanca, which was 5.0. The dot product corresponding to that cell in the recommendation matrix should hopefully be around 5.0, and it is: (1.1 * 0.9) + (2.3 * 1.7) = 4.9 More importantly, will User 1 like Black Panther? Taking the dot product corresponding to the first row and the third column yields a predicted rating of 4.3: (1.1 * 1.4) + (2.3 * 1.2) = 4.3 Matrix factorization typically yields a user matrix and item matrix that, together, are significantly more compact than the target matrix. Mean Absolute Error (MAE)The average loss per example when L1 loss is used. Calculate Mean Absolute Error as follows:
Click the icon to see the formal math.$$\text{Mean Absolute Error} = \frac{1}{n}\sum_{i=0}^n | y_i - \hat{y}_i |$$ where:
For example, consider the calculation of L1 loss on the following batch of five examples:
So, L1 loss is 8 and the number of examples is 5. Therefore, the Mean Absolute Error is: Mean Absolute Error = L1 loss / Number of Examples Mean Absolute Error = 8/5 = 1.6 Contrast Mean Absolute Error with Mean Squared Error and Root Mean Squared Error. Mean Squared Error (MSE)The average loss per example when L2 loss is used. Calculate Mean Squared Error as follows:
Click the icon to see the formal math.$$\text{Mean Squared Error} = \frac{1}{n}\sum_{i=0}^n {(y_i - \hat{y}_i)}^2$$ where:
For example, consider the loss on the following batch of five examples:
Therefore, the Mean Squared Error is: Mean Squared Error = L2 loss / Number of Examples Mean Squared Error = 16/5 = 3.2 Mean Squared Error is a popular training optimizer, particularly for linear regression. Contrast Mean Squared Error with Mean Absolute Error and Root Mean Squared Error. TensorFlow Playground uses Mean Squared Error to calculate loss values. Click the icon to see more details about outliers.Outliers strongly influence Mean Squared Error. For example, a loss of 1 is a squared loss of 1, but a loss of 3 is a squared loss of 9. In the preceding table, the example with a loss of 3 accounts for ~56% of the Mean Squared Error, while each of the examples with a loss of 1 accounts for only 6% of the Mean Squared Error. Outliers do not influence Mean Absolute Error as strongly as Mean Squared Error. For example, a loss of 3 accounts for only ~38% of the Mean Absolute Error. Clipping is one way to prevent extreme outliers from damaging your model's predictive ability. metricA statistic that you care about. An objective is a metric that a machine learning system tries to optimize. A subset of machine learning that discovers or improves a learning algorithm. A meta-learning system can also aim to train a model to quickly learn a new task from a small amount of data or from experience gained in previous tasks. Meta-learning algorithms generally try to achieve the following:
Meta-learning is related to few-shot learning. Metrics API (tf.metrics)A TensorFlow API for evaluating models. For example, mini-batchA small, randomly selected subset of a batch processed in one iteration. The batch size of a mini-batch is usually between 10 and 1,000 examples. For example, suppose the entire training set (the full batch) consists of 1,000 examples. Further suppose that you set the batch size of each mini-batch to 20. Therefore, each iteration determines the loss on a random 20 of the 1,000 examples and then adjusts the weights and biases accordingly. It is much more efficient to calculate the loss on a mini-batch than the loss on all the examples in the full batch. mini-batch stochastic gradient descentA gradient descent algorithm that uses mini-batches. In other words, mini-batch stochastic gradient descent estimates the gradient based on a small subset of the training data. Regular stochastic gradient descent uses a mini-batch of size 1. minimax lossA loss function for generative adversarial networks, based on the cross-entropy between the distribution of generated data and real data. Minimax loss is used in the first paper to describe generative adversarial networks. minority classThe less common label in a class-imbalanced dataset. For example, given a dataset containing 99% negative labels and 1% positive labels, the positive labels are the minority class. Contrast with majority class. Click the icon for additional notes.A training set with a million examples sounds impressive. However, if the minority class is poorly represented, then even a very large training set might be insufficient. Focus less on the total number of examples in the dataset and more on the number of examples in the minority class. If your dataset doesn't contain enough minority class examples, consider using downsampling (the definition in the second bullet) to supplement the minority class. MLAbbreviation for machine learning. MNISTA public-domain dataset compiled by LeCun, Cortes, and Burges containing 60,000 images, each image showing how a human manually wrote a particular digit from 0–9. Each image is stored as a 28x28 array of integers, where each integer is a grayscale value between 0 and 255, inclusive. MNIST is a canonical dataset for machine learning, often used to test new machine learning approaches. For details, see The MNIST Database of Handwritten Digits. modalityA high-level data category. For example, numbers, text, images, video, and audio are five different modalities. modelIn general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions. In supervised machine learning, a model takes an example as input and infers a prediction as output. Within supervised machine learning, models differ somewhat. For example:
You can save, restore, or make copies of a model. Unsupervised machine learning also generates models, typically a function that can map an input example to the most appropriate cluster. Click the icon to compare algebraic and programming functions to ML models.An algebraic function such as the following is a model: f(x, y) = 3x -5xy + y2 + 17 The preceding function maps input values (x and y) to output. Similarly, a programming function like the following is also a model: def half_of_greater(x, y): if (x > y): return(x / 2) else return(y / 2) A caller passes arguments to the preceding Python function, and the Python function generates output (via the return statement). Although a deep neural network has a very different mathematical structure than an algebraic or programming function, a deep neural network still takes input (an example) and returns output (a prediction). A human programmer codes a programming function manually. In contrast, a machine learning model gradually learns the optimal parameters during automated training. model capacityThe complexity of problems that a model can learn. The more complex the problems that a model can learn, the higher the model’s capacity. A model’s capacity typically increases with the number of model parameters. For a formal definition of classifier capacity, see VC dimension. model parallelismA way of scaling training or inference that puts different parts of one model on different devices. Model parallelism enables models that are too big to fit on a single device. See also data parallelism. model trainingThe process of determining the best model. MomentumA sophisticated gradient descent algorithm in which a learning step depends not only on the derivative in the current step, but also on the derivatives of the step(s) that immediately preceded it. Momentum involves computing an exponentially weighted moving average of the gradients over time, analogous to momentum in physics. Momentum sometimes prevents learning from getting stuck in local minima. multi-class classificationIn supervised learning, a classification problem in which the dataset contains more than two classes of labels. For example, the labels in the Iris dataset must be one of the following three classes:
A model trained on the Iris dataset that predicts Iris type on new examples is performing multi-class classification. In contrast, classification problems that distinguish between exactly two classes are binary classification models. For example, an email model that predicts either spam or not spam is a binary classification model. In clustering problems, multi-class classification refers to more than two clusters. multi-class logistic regressionUsing logistic regression in multi-class classification problems. multi-head self-attentionAn extension of self-attention that applies the self-attention mechanism multiple times for each position in the input sequence. Transformers introduced multi-head self-attention. multimodal modelA model whose inputs and/or outputs include more than one modality. For example, consider a model that takes both an image and a text caption (two modalities) as features, and outputs a score indicating how appropriate the text caption is for the image. So, this model's inputs are multimodal and the output is unimodal. multinomial classificationSynonym for multi-class classification. multinomial regressionSynonym for multi-class logistic regression. NNaN trapWhen one number in your model becomes a NaN during training, which causes many or all other numbers in your model to eventually become a NaN. NaN is an abbreviation for Not a Number. natural language understandingDetermining a user's intentions based on what the user typed or said. For example, a search engine uses natural language understanding to determine what the user is searching for based on what the user typed or said. negative classIn binary classification, one class is termed positive and the other is termed negative. The positive class is the thing or event that the model is testing for and the negative class is the other possibility. For example:
Contrast with positive class. neural networkA model containing at least one hidden layer. A deep neural network is a type of neural network containing more than one hidden layer. For example, the following diagram shows a deep neural network containing two hidden layers.
Each neuron in a neural network connects to all of the nodes in the next layer. For example, in the preceding diagram, notice that each of the three neurons in the first hidden layer separately connect to both of the two neurons in the second hidden layer. Neural networks implemented on computers are sometimes called artificial neural networks to differentiate them from neural networks found in brains and other nervous systems. Some neural networks can mimic extremely complex nonlinear relationships between different features and the label. See also convolutional neural network and recurrent neural network. neuronIn machine learning, a distinct unit within a hidden layer of a neural network. Each neuron performs the following two-step action:
A neuron in the first hidden layer accepts inputs from the feature values in the input layer. A neuron in any hidden layer beyond the first accepts inputs from the neurons in the preceding hidden layer. For example, a neuron in the second hidden layer accepts inputs from the neurons in the first hidden layer. The following illustration highlights two neurons and their inputs.
A neuron in a neural network mimics the behavior of neurons in brains and other parts of nervous systems. N-gramAn ordered sequence of N words. For example, truly madly is a 2-gram. Because order is relevant, madly truly is a different 2-gram than truly madly.
Many natural language understanding models rely on N-grams to predict the next word that the user will type or say. For example, suppose a user typed three blind. An NLU model based on trigrams would likely predict that the user will next type mice. Contrast N-grams with bag of words, which are unordered sets of words. NLUAbbreviation for natural language understanding. node (neural network)A neuron in a hidden layer. node (TensorFlow graph)An operation in a TensorFlow graph. node (decision tree)In a decision tree, any condition or leaf.
noiseBroadly speaking, anything that obscures the signal in a dataset. Noise can be introduced into data in a variety of ways. For example:
non-binary conditionA condition containing more than two possible outcomes. For example, the following non-binary condition contains three possible outcomes:
nonlinearA relationship between two or more variables that can't be represented solely through addition and multiplication. A linear relationship can be represented as a line; a nonlinear relationship can't be represented as a line. For example, consider two models that each relate a single feature to a single label. The model on the left is linear and the model on the right is nonlinear:
non-response biasSee selection bias. nonstationarityA feature whose values change across one or more dimensions, usually time. For example, consider the following examples of nonstationarity:
Contrast with stationarity. normalizationBroadly speaking, the process of converting a variable's actual range of values into a standard range of values, such as:
For example, suppose the actual range of values of a certain feature is 800 to 2,400. As part of feature engineering, you could normalize the actual values down to a standard range, such as -1 to +1. Normalization is a common task in feature engineering. Models usually train faster (and produce better predictions) when every numerical feature in the feature vector has roughly the same range. novelty detectionThe process of determining whether a new (novel) example comes from the same distribution as the training set. In other words, after training on the training set, novelty detection determines whether a new example (during inference or during additional training) is an outlier. Contrast with outlier detection. numerical dataFeatures represented as integers or real-valued numbers. For example, a house valuation model would probably represent the size of a house (in square feet or square meters) as numerical data. Representing a feature as numerical data indicates that the feature's values have a mathematical relationship to the label. That is, the number of square meters in a house probably has some mathematical relationship to the value of the house. Not all integer data should be represented as numerical data. For example, postal codes in some parts of the world are integers; however, integer postal codes should not be represented as numerical data in models.
That's because a postal code of Numerical features are sometimes called continuous features. NumPyAn open-source math library that provides efficient array operations in Python. pandas is built on NumPy. OobjectiveA metric that your algorithm is trying to optimize. objective functionThe mathematical formula or metric that a model aims to optimize. For example, the objective function for linear regression is usually Mean Squared Loss. Therefore, when training a linear regression model, training aims to minimize Mean Squared Loss. In some cases, the goal is to maximize the objective function. For example, if the objective function is accuracy, the goal is to maximize accuracy. See also loss. oblique conditionIn a decision tree, a condition that involves more than one feature. For example, if height and width are both features, then the following is an oblique condition:
Contrast with axis-aligned condition. offlineSynonym for static. offline inferenceThe process of a model generating a batch of predictions and then caching (saving) those predictions. Apps can then access the desired prediction from the cache rather than rerunning the model. For example, consider a model that generates local weather forecasts (predictions) once every four hours. After each model run, the system caches all the local weather forecasts. Weather apps retrieve the forecasts from the cache. Offline inference is also called static inference. Contrast with online inference. one-hot encodingRepresenting categorical data as a vector in which:
One-hot encoding is commonly
used to represent strings or identifiers that have a finite set of possible values. For example, suppose a certain categorical feature named
One-hot encoding could represent each of the five values as follows:
Thanks to one-hot encoding, a model can learn different connections based on each of the five countries. Representing a feature as numerical data is an alternative to one-hot encoding. Unfortunately, representing the Scandinavian countries numerically is not a good choice. For example, consider the following numeric representation:
With numeric encoding, a model would interpret the raw numbers mathematically and would try to train on those numbers. However, Iceland isn't actually twice as much (or half as much) of something as Norway, so the model would come to some strange conclusions. one-shot learningA machine learning approach, often used for object classification, designed to learn effective classifiers from a single training example. See also few-shot learning. one-vs.-allGiven a classification problem with N classes, a solution consisting of N separate binary classifiers—one binary classifier for each possible outcome. For example, given a model that classifies examples as animal, vegetable, or mineral, a one-vs.-all solution would provide the following three separate binary classifiers:
onlineSynonym for dynamic. online inferenceGenerating predictions on demand. For example, suppose an app passes input to a model and issues a request for a prediction. A system using online inference responds to the request by running the model (and returning the prediction to the app). Contrast with offline inference. operation (op)In TensorFlow, any procedure that creates, manipulates, or destroys a Tensor. For example, a matrix multiply is an operation that takes two Tensors as input and generates one Tensor as output. out-of-bag evaluation (OOB evaluation)A mechanism for evaluating the quality of a decision forest by testing each decision tree against the examples not used during training of that decision tree. For example, in the following diagram, notice that the system trains each decision tree on about two-thirds of the examples and then evaluates against the remaining one-third of the examples.
Out-of-bag evaluation is a computationally efficient and conservative approximation of the cross-validation mechanism. In cross-validation, one model is trained for each cross-validation round (for example, 10 models are trained in a 10-fold cross-validation). With OOB evaluation, a single model is trained. Because bagging withholds some data from each tree during training, OOB evaluation can use that data to approximate cross-validation. optimizerA specific implementation of the gradient descent algorithm. Popular optimizers include:
out-group homogeneity biasThe tendency to see out-group members as more alike than in-group members when comparing attitudes, values, personality traits, and other characteristics. In-group refers to people you interact with regularly; out-group refers to people you do not interact with regularly. If you create a dataset by asking people to provide attributes about out-groups, those attributes may be less nuanced and more stereotyped than attributes that participants list for people in their in-group. For example, Lilliputians might describe the houses of other Lilliputians in great detail, citing small differences in architectural styles, windows, doors, and sizes. However, the same Lilliputians might simply declare that Brobdingnagians all live in identical houses. Out-group homogeneity bias is a form of group attribution bias. See also in-group bias. outlier detectionThe process of identifying outliers in a training set. Contrast with novelty detection. outliersValues distant from most other values. In machine learning, any of the following are outliers:
For example, suppose that Outliers are often caused by typos or other input mistakes. In other cases, outliers aren't mistakes; after all, values five standard deviations away from the mean are rare but hardly impossible. Outliers often cause problems in model training. Clipping is one way of managing outliers. output layerThe "final" layer of a neural network. The output layer contains the prediction. The following illustration shows a small deep neural network with an input layer, two hidden layers, and an output layer:
overfittingCreating a model that matches the training data so closely that the model fails to make correct predictions on new data. Regularization can reduce overfitting. Training on a large and diverse training set can also reduce overfitting. Click the icon for additional notes.Overfitting is like strictly following advice from only your favorite teacher. You'll probably be successful in that teacher's class, but you might "overfit" to that teacher's ideas and be unsuccessful in other classes. Following advice from a mixture of teachers will enable you to adapt better to new situations. oversamplingReusing the examples of a minority class in a class-imbalanced dataset in order to create a more balanced training set. For example, consider a binary classification problem in which the ratio of the majority class to the minority class is 5,000:1. If the dataset contains a million examples, then the dataset contains only about 200 examples of the minority class, which might be too few examples for effective training. To overcome this deficiency, you might oversample (reuse) those 200 examples multiple times, possibly yielding sufficient examples for useful training. You need to be careful about over overfitting when oversampling. Contrast with undersampling. PpandasA column-oriented data analysis API built on top of numpy. Many machine learning frameworks, including TensorFlow, support pandas data structures as inputs. See the pandas documentation for details. parameterThe weights and biases that a model learns during training. For example, in a linear regression model, the parameters consist of the bias (b) and all the weights (w1, w2, and so on) in the following formula: $$y' = b + w_1x_1 + w_2x_2 + … w_nx_n$$ In contrast, hyperparameter are the values that you (or a hyperparameter turning service) supply to the model. For example, learning rate is a hyperparameter. Parameter Server (PS)A job that keeps track of a model's parameters in a distributed setting. parameter updateThe operation of adjusting a model's parameters during training, typically within a single iteration of gradient descent. partial derivativeA derivative in which all but one of the variables is considered a constant. For example, the partial derivative of f(x, y) with respect to x is the derivative of f considered as a function of x alone (that is, keeping y constant). The partial derivative of f with respect to x focuses only on how x is changing and ignores all other variables in the equation. participation biasSynonym for non-response bias. See selection bias. partitioning strategyThe algorithm by which variables are divided across parameter servers. perceptronA system (either hardware or software) that takes in one or more input values, runs a function on the weighted sum of the inputs, and computes a single output value. In machine learning, the function is typically nonlinear, such as ReLU, sigmoid, or tanh. For example, the following perceptron relies on the sigmoid function to process three input values: $$f(x_1, x_2, x_3) = \text{sigmoid}(w_1 x_1 + w_2 x_2 + w_3 x_3)$$ In the following illustration, the perceptron takes three inputs, each of which is itself modified by a weight before entering the perceptron:
Perceptrons are the neurons in neural networks. performanceOverloaded term with the following meanings:
permutation variable importancesA type of variable importance that evaluates the increase in the prediction error of a model after permuting the feature’s values. Permutation variable importance is a model agnostic metric. perplexityOne measure of how well a model is accomplishing its task. For example, suppose your task is to read the first few letters of a word a user is typing on a smartphone keyboard, and to offer a list of possible completion words. Perplexity, P, for this task is approximately the number of guesses you need to offer in order for your list to contain the actual word the user is trying to type. Perplexity is related to cross-entropy as follows: $$P= 2^{-\text{cross entropy}}$$ pipelineThe infrastructure surrounding a machine learning algorithm. A pipeline includes gathering the data, putting the data into training data files, training one or more models, and exporting the models to production. pipeliningA form of model parallelism in which a model's processing is divided into consecutive stages and each stage is executed on a different device. While a stage is processing one batch, the preceding stage can work on the next batch. See also staged training. policyIn reinforcement learning, an agent's probabilistic mapping from states to actions. poolingReducing a matrix (or matrices) created by an earlier convolutional layer to a smaller matrix. Pooling usually involves taking either the maximum or average value across the pooled area. For example, suppose we have the following 3x3 matrix:
A pooling operation, just like a convolutional operation, divides that matrix into slices and then slides that convolutional operation by strides. For example, suppose the pooling operation divides the convolutional matrix into 2x2 slices with a 1x1 stride. As the following diagram illustrates, four pooling operations take place. Imagine that each pooling operation picks the maximum value of the four in that slice:
Pooling helps enforce translational invariance in the input matrix. Pooling for vision applications is known more formally as spatial pooling. Time-series applications usually refer to pooling as temporal pooling. Less formally, pooling is often called subsampling or downsampling. positive classThe class you are testing for. For example, the positive class in a cancer model might be "tumor." The positive class in an email classifier might be "spam." Contrast with negative class. Click the icon for additional notes.The term positive class can be confusing because the "positive" outcome of many tests is often an undesirable result. For example, the positive class in many medical tests corresponds to tumors or diseases. In general, you want a doctor to tell you, "Congratulations! Your test results were negative." Regardless, the positive class is the event that the test is seeking to find. Admittedly, you're simultaneously testing for both the positive and negative classes. post-processingAdjusting the output of a model after the model has been run. Post-processing can be used to enforce fairness constraints without modifying models themselves. For example, one might apply post-processing to a binary classifier by setting a classification threshold such that equality of opportunity is maintained for some attribute by checking that the true positive rate is the same for all values of that attribute. PR AUC (area under the PR curve)Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model. precisionA metric for classification models that answers the following question:
Here is the formula: $$\text{Precision} = \frac{\text{true positives}} {\text{true positives} + \text{false positives}}$$ where:
For example, suppose a model made 200 positive predictions. Of these 200 positive predictions:
In this case: $$\text{Precision} = \frac{\text{150}} {\text{150} + \text{50}} = 0.75$$ Contrast with accuracy and recall. precision-recall curveA curve of precision vs. recall at different classification thresholds. predictionA model's output. For example:
prediction biasA value indicating how far apart the average of predictions is from the average of labels in the dataset. Not to be confused with the bias term in machine learning models or with bias in ethics and fairness. predictive parityA fairness metric that checks whether, for a given classifier, the precision rates are equivalent for subgroups under consideration. For example, a model that predicts college acceptance would satisfy predictive parity for nationality if its precision rate is the same for Lilliputians and Brobdingnagians. Predictive parity is sometime also called predictive rate parity. See "Fairness Definitions Explained" (section 3.2.1) for a more detailed discussion of predictive parity. predictive rate parityAnother name for predictive parity. preprocessingProcessing data before it's used to train a model. Preprocessing could be as simple as removing words from an English text corpus that don't occur in the English dictionary, or could be as complex as re-expressing data points in a way that eliminates as many attributes that are correlated with sensitive attributes as possible. Preprocessing can help satisfy fairness constraints. pre-trained modelModels or model components (such as embedding vector) that have been already been trained. Sometimes, you'll feed pre-trained embedding vectors into a neural network. Other times, your model will train the embedding vectors itself rather than rely on the pre-trained embeddings. prior beliefWhat you believe about the data before you begin training on it. For example, L2 regularization relies on a prior belief that weights should be small and normally distributed around zero. probabilistic regression modelA regression model that uses not only the weights for each feature, but also the uncertainty of those weights. A probabilistic regression model generates a prediction and the uncertainty of that prediction. For example, a probabilistic regression model might yield a prediction of 325 with a standard deviation of 12. For more information about probabilistic regression models, see this Colab on tensorflow.org. proxy (sensitive attributes)An attribute used as a stand-in for a sensitive attribute. For example, an individual's postal code might be used as a proxy for their income, race, or ethnicity. proxy labelsData used to approximate labels not directly available in a dataset. For example, suppose you must train a model to predict employee stress level. Your dataset contains a lot of predictive features but doesn't contain a label named stress level. Undaunted, you pick "workplace accidents" as a proxy label for stress level. After all, employees under high stress get into more accidents than calm employees. Or do they? Maybe workplace accidents actually rise and fall for multiple reasons. As a second example, suppose you want is it raining? to be a Boolean label for your dataset, but your dataset doesn't contain rain data. If photographs are available, you might establish pictures of people carrying umbrellas as a proxy label for is it raining? Is that a good proxy label? Possibly, but people in some cultures may be more likely to carry umbrellas to protect against sun than the rain. Proxy labels are often imperfect. When possible, choose actual labels over proxy labels. That said, when an actual label is absent, pick the proxy label very carefully, choosing the least horrible proxy label candidate. QQ-functionIn reinforcement learning, the function that predicts the expected return from taking an action in a state and then following a given policy. Q-function is also known as state-action value function. Q-learningIn reinforcement learning, an algorithm that allows an agent to learn the optimal Q-function of a Markov decision process by applying the Bellman equation. The Markov decision process models an environment. quantileEach bucket in quantile bucketing. quantile bucketingDistributing a feature's values into buckets so that each bucket contains the same (or almost the same) number of examples. For example, the following figure divides 44 points into 4 buckets, each of which contains 11 points. In order for each bucket in the figure to contain the same number of points, some buckets span a different width of x-values.
quantizationAn algorithm that implements quantile bucketing on a particular feature in a dataset. queueA TensorFlow Operation that implements a queue data structure. Typically used in I/O. Rrandom forestAn ensemble of decision trees in which each decision tree is trained with a specific random noise, such as bagging. Random forests are a type of decision forest. random policyIn reinforcement learning, a policy that chooses an action at random. rankingA type of supervised learning whose objective is to order a list of items. rank (ordinality)The ordinal position of a class in a machine learning problem that categorizes classes from highest to lowest. For example, a behavior ranking system could rank a dog's rewards from highest (a steak) to lowest (wilted kale). rank (Tensor)The number of dimensions in a Tensor. For instance, a scalar has rank 0, a vector has rank 1, and a matrix has rank 2. Not to be confused with rank (ordinality). raterA human who provides labels for examples. "Annotator" is another name for rater. recallA metric for classification models that answers the following question:
Here is the formula: \[\text{Recall} = \frac{\text{true positives}} {\text{true positives} + \text{false negatives}} \] where:
For instance, suppose your model made 200 predictions on examples for which ground truth was the positive class. Of these 200 predictions:
In this case: \[\text{Recall} = \frac{\text{180}} {\text{180} + \text{20}} = 0.9 \] Click the icon for notes about class-imbalanced datasets.Recall is particularly useful for determining the predictive power of classification models in which the positive class is rare. For example, consider a class-imbalanced dataset in which the positive class for a certain disease occurs in only 10 patients out of a million. Suppose your model makes five million predictions that yield the following outcomes:
The recall of this model is therefore: recall = TP / (TP + FN) recall = 30 / (30 + 20) = 0.6 = 60% By contrast, the accuracy of this model is: accuracy = (TP + TN) / (TP + TN + FP + FN) accuracy = (30 + 4,999,000) / (30 + 4,999,000 + 950 + 20) = 99.98% That high value of accuracy looks impressive but is essentially meaningless. Recall is a much more useful metric for class-imbalanced datasets than accuracy. recommendation systemA system that selects for each user a relatively small set of desirable items from a large corpus. For example, a video recommendation system might recommend two videos from a corpus of 100,000 videos, selecting Casablanca and The Philadelphia Story for one user, and Wonder Woman and Black Panther for another. A video recommendation system might base its recommendations on factors such as:
Rectified Linear Unit (ReLU)An activation function with the following behavior:
For example:
Here is a plot of ReLU:
ReLU is a very popular activation function. Despite its simple behavior, ReLU still enables a neural network to learn nonlinear relationships between features and the label. recurrent neural networkA neural network that is intentionally run multiple times, where parts of each run feed into the next run. Specifically, hidden layers from the previous run provide part of the input to the same hidden layer in the next run. Recurrent neural networks are particularly useful for evaluating sequences, so that the hidden layers can learn from previous runs of the neural network on earlier parts of the sequence. For example, the following figure shows a recurrent neural network that runs four times. Notice that the values learned in the hidden layers from the first run become part of the input to the same hidden layers in the second run. Similarly, the values learned in the hidden layer on the second run become part of the input to the same hidden layer in the third run. In this way, the recurrent neural network gradually trains and predicts the meaning of the entire sequence rather than just the meaning of individual words.
regression modelInformally, a model that generates a numerical prediction. (In contrast, a classification model generates a class prediction.) For example, the following are all regression models:
Two common types of regression models are:
Not every model that outputs numerical predictions is a regression model. In some cases, a numeric prediction is really just a classification model that happens to have numeric class names. For example, a model that predicts a numeric postal code is a classification model, not a regression model. regularizationAny mechanism that reduces overfitting. Popular types of regularization include:
Regularization can also be defined as the penalty on a model's complexity. Click the icon for additional notes.Regularization is counterintuitive. Increasing regularization usually increases training loss, which is confusing because, well, isn't the goal to minimize training loss? Actually, no. The goal isn't to minimize training loss. The goal is to make excellent predictions on real-world examples. Remarkably, even though increasing regularization increases training loss, it usually helps models make better predictions on real-world examples. regularization rateA number that specifies the relative importance of regularization during training. Raising the regularization rate reduces overfitting but may reduce the model's predictive power. Conversely, reducing or omitting the regularization rate increases overfitting. Click the icon to see the math.The regularization rate is usually represented as the Greek letter lambda. The following simplified loss equation shows lambda's influence: $$\text{minimize(loss function + }\lambda\text{(regularization))}$$ where regularization is any regularization mechanism, including;
reinforcement learning (RL)A family of algorithms that learn an optimal policy, whose goal is to maximize return when interacting with an environment. For example, the ultimate reward of most games is victory. Reinforcement learning systems can become expert at playing complex games by evaluating sequences of previous game moves that ultimately led to wins and sequences that ultimately led to losses. ReLUAbbreviation for Rectified Linear Unit. replay bufferIn DQN-like algorithms, the memory used by the agent to store state transitions for use in experience replay. reporting biasThe fact that the frequency with which people write about actions, outcomes, or properties is not a reflection of their real-world frequencies or the degree to which a property is characteristic of a class of individuals. Reporting bias can influence the composition of data that machine learning systems learn from. For example, in books, the word laughed is more prevalent than breathed. A machine learning model that estimates the relative frequency of laughing and breathing from a book corpus would probably determine that laughing is more common than breathing. representationThe process of mapping data to useful features. re-rankingThe final stage of a recommendation system, during which scored items may be re-graded according to some other (typically, non-ML) algorithm. Re-ranking evaluates the list of items generated by the scoring phase, taking actions such as:
returnIn reinforcement learning, given a certain policy and a certain state, the return is the sum of all rewards that the agent expects to receive when following the policy from the state to the end of the episode. The agent accounts for the delayed nature of expected rewards by discounting rewards according to the state transitions required to obtain the reward. Therefore, if the discount factor is \(\gamma\), and \(r_0, \ldots, r_{N}\) denote the rewards until the end of the episode, then the return calculation is as follows: $$\text{Return} = r_0 + \gamma r_1 + \gamma^2 r_2 + \ldots + \gamma^{N-1} r_{N-1}$$ rewardIn reinforcement learning, the numerical result of taking an action in a state, as defined by the environment. ridge regularizationSynonym for L2 regularization. The term ridge regularization is more frequently used in pure statistics contexts, whereas L2 regularization is used more often in machine learning. RNNAbbreviation for recurrent neural networks. ROC (receiver operating characteristic) CurveA graph of true positive rate vs. false positive rate for different classification thresholds in binary classification. The shape of an ROC curve suggests a binary classification model's ability to separate positive classes from negative classes. Suppose, for example, that a binary classification model perfectly separates all the negative classes from all the positive classes:
The ROC curve for the preceding model looks as follows:
In contrast, the following illustration graphs the raw logistic regression values for a terrible model that can't separate negative classes from positive classes at all:
The ROC curve for this model looks as follows:
Meanwhile, back in the real world, most binary classification models separate positive and negative classes to some degree, but usually not perfectly. So, a typical ROC curve falls somewhere between the two extremes:
The point on an ROC curve closest to (0.0,1.0) theoretically identifies the ideal classification threshold. However, several other real-world issues influence the selection of the ideal classification threshold. For example, perhaps false negatives cause far more pain than false positives. A numerical metric called AUC summarizes the ROC curve into a single floating-point value. rootThe starting node (the first condition) in a decision tree. By convention, diagrams put the root at the top of the decision tree. For example:
root directoryThe directory you specify for hosting subdirectories of the TensorFlow checkpoint and events files of multiple models. Root Mean Squared Error (RMSE)The square root of the Mean Squared Error. rotational invarianceIn an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes. For example, the algorithm can still identify a tennis racket whether it is pointing up, sideways, or down. Note that rotational invariance is not always desirable; for example, an upside-down 9 should not be classified as a 9. See also translational invariance and size invariance. Ssampling biasSee selection bias. sampling with replacementA method of picking items from a set of candidate items in which the same item can be picked multiple times. The phrase "with replacement" means that after each selection, the selected item is returned to the pool of candidate items. The inverse method, sampling without replacement, means that a candidate item can only be picked once. For example, consider the following fruit set: fruit = {kiwi, apple, pear, fig, cherry, lime, mango} Suppose that the system randomly picks fruit = {kiwi, apple, pear, fig, cherry, lime, mango} Yes, that's the same set as before, so the system could potentially pick If using sampling without
replacement, once picked, a sample can't be picked again. For example, if the system randomly picks fruit = {kiwi, apple, pear, cherry, lime, mango} Click the icon for additional notes.The word replacement in sampling with replacement confuses many people. In English, replacement means "substitution." However, sampling with replacement actually uses the French definition for replacement, which means "putting something back." The English word replacement is translated as the French word remplacement. SavedModelThe recommended format for saving and recovering TensorFlow models. SavedModel is a language-neutral, recoverable serialization format, which enables higher-level systems and tools to produce, consume, and transform TensorFlow models. See the Saving and Restoring chapter in the TensorFlow Programmer's Guide for complete details. SaverA TensorFlow object responsible for saving model checkpoints. scalarA single number or a single string that can be represented as a tensor of rank 0. For example, the following lines of code each create one scalar in TensorFlow: breed = tf.Variable("poodle", tf.string) temperature = tf.Variable(27, tf.int16) precision = tf.Variable(0.982375101275, tf.float64) scalingAny mathematical transform or technique that shifts the range of a label and/or feature value. Some forms of scaling are very useful for transformations like normalization. Common forms of scaling useful in Machine Learning include:
scikit-learnA popular open-source machine learning platform. See scikit-learn.org. scoringThe part of a recommendation system that provides a value or ranking for each item produced by the candidate generation phase. selection biasErrors in conclusions drawn from sampled data due to a selection process that generates systematic differences between samples observed in the data and those not observed. The following forms of selection bias exist:
For example, suppose you are creating a machine learning model that predicts people's enjoyment of a movie. To collect training data, you hand out a survey to everyone in the front row of a theater showing the movie. Offhand, this may sound like a reasonable way to gather a dataset; however, this form of data collection may introduce the following forms of selection bias:
self-attention (also called self-attention layer)A neural network layer that transforms a sequence of embeddings (for instance, token embeddings) into another sequence of embeddings. Each embedding in the output sequence is constructed by integrating information from the elements of the input sequence through an attention mechanism. The self part of self-attention refers to the sequence attending to itself rather than to some other context. Self-attention is one of the main building blocks for Transformers and uses dictionary lookup terminology, such as “query”, “key”, and “value”. A self-attention layer starts with a sequence of input representations, one for each word. The input representation for a word can be a simple embedding. For each word in an input sequence, the network scores the relevance of the word to every element in the whole sequence of words. The relevance scores determine how much the word's final representation incorporates the representations of other words. For example, consider the following sentence:
The following illustration (from Transformer: A Novel Neural Network Architecture for Language Understanding) shows a self-attention layer's attention pattern for the pronoun it, with the darkness of each line indicating how much each word contributes to the representation:
The self-attention layer highlights words that are relevant to "it". In this case, the attention layer has learned to highlight words that it might refer to, assigning the highest weight to animal. For a sequence of n tokens, self-attention transforms a sequence of embeddings n separate times, once at each position in the sequence. Refer also to attention and multi-head self-attention. self-supervised learningA family of techniques for converting an unsupervised machine learning problem into a supervised machine learning problem by creating surrogate labels from unlabeled examples. Some Transformer-based models such as BERT use self-supervised learning. Self-supervised training is a semi-supervised learning approach. self-trainingA variant of self-supervised learning that is particularly useful when all of the following conditions are true:
Self-training works by iterating over the following two steps until the model stops improving:
Notice that each iteration of Step 2 adds more labeled examples for Step 1 to train on. semi-supervised learningTraining a model on data where some of the training examples have labels but others don't. One technique for semi-supervised learning is to infer labels for the unlabeled examples, and then to train on the inferred labels to create a new model. Semi-supervised learning can be useful if labels are expensive to obtain but unlabeled examples are plentiful. Self-training is one technique for semi-supervised learning. sensitive attributeA human attribute that may be given special consideration for legal, ethical, social, or personal reasons. sentiment analysisUsing statistical or machine learning algorithms to determine a group's overall attitude—positive or negative—toward a service, product, organization, or topic. For example, using natural language understanding, an algorithm could perform sentiment analysis on the textual feedback from a university course to determine the degree to which students generally liked or disliked the course. sequence modelA model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos. sequence-to-sequence taskA task that converts an input sequence of tokens to an output sequence of tokens. For example, two popular kinds of sequence-to-sequence tasks are:
servingA synonym for inferring. shape (Tensor)The number of elements in each dimension of a tensor. The shape is represented as a list of integers. For example, the following two-dimensional tensor has a shape of [3,4]: [[5, 7, 6, 4], [2, 9, 4, 8], [3, 6, 5, 1]] TensorFlow uses row-major (C-style) format to represent the order of dimensions, which is why the shape in TensorFlow is [3,4] rather than [4,3]. In other words, in a two-dimensional TensorFlow Tensor, the shape is [number of rows, number of columns]. shrinkageA hyperparameter in gradient boosting that controls overfitting. Shrinkage in gradient boosting is analogous to learning rate in gradient descent. Shrinkage is a decimal value between 0.0 and 1.0. A lower shrinkage value reduces overfitting more than a larger shrinkage value. sigmoid functionA mathematical function that "squishes" an input value into a constrained range, typically 0 to 1 or -1 to +1. That is, you can pass any number (two, a million, negative billion, whatever) to a sigmoid and the output will still be in the constrained range. A plot of the sigmoid activation function looks as follows:
The sigmoid function has several uses in machine learning, including:
Click the icon to see the math.The sigmoid function over an input number x has the following formula: $$ sigmoid(x) = \frac{1}{1 + e^{-\text{x}}} $$ In machine learning, x is generally a weighted sum. similarity measureIn clustering algorithms, the metric used to determine how alike (how similar) any two examples are. size invarianceIn an image classification problem, an algorithm's ability to successfully classify images even when the size of the image changes. For example, the algorithm can still identify a cat whether it consumes 2M pixels or 200K pixels. Note that even the best image classification algorithms still have practical limits on size invariance. For example, an algorithm (or human) is unlikely to correctly classify a cat image consuming only 20 pixels. See also translational invariance and rotational invariance. sketchingIn unsupervised machine learning, a category of algorithms that perform a preliminary similarity analysis on examples. Sketching algorithms use a locality-sensitive hash function to identify points that are likely to be similar, and then group them into buckets. Sketching decreases the computation required for similarity calculations on large datasets. Instead of calculating similarity for every single pair of examples in the dataset, we calculate similarity only for each pair of points within each bucket. softmaxA function that determines probabilities for each possible class in a multi-class classification model. The probabilities add up to exactly 1.0. For example, the following table shows how softmax distributes various probabilities:
Softmax is also called full softmax. Contrast with candidate sampling. Click the icon to see the math.The softmax equation is as follows: $$\sigma_i = \frac{e^{\text{z}_i}} {\sum_{j=1}^{j=K} {e^{\text{z}_j}}} $$ where:
For example, suppose the input vector is: [1.2, 2.5, 1.8] Therefore, softmax calculates the denominator as follows: $$\text{denominator} = e^{1.2} + e^{2.5} + e^{1.8} = 21.552$$ The softmax probability of each element is therefore: $$\sigma_1 = \frac{e^{1.2}}{21.552} = 0.154 $$ $$\sigma_2 = \frac{e^{2.5}}{21.552} = 0.565 $$ $$\sigma_1 = \frac{e^{1.8}}{21.552} = 0.281 $$ So, the output vector is therefore: $$\sigma = [0.154, 0.565, 0.281]$$ The sum of the three elements in $\sigma$ is 1.0. Phew! sparse featureA feature whose values are predominately zero or empty. For example, a feature containing a single 1 value and a million 0 values is sparse. In contrast, a dense feature has values that are predominantly not zero or empty. In machine learning, a surprising number of features are sparse features. Categorical features are usually sparse features. For example, of the 300 possible tree species in a forest, a single example might identify just a maple tree. Or, of the millions of possible videos in a video library, a single example might identify just "Casablanca." In a model, you typically represent sparse features with one-hot encoding. If the one-hot encoding is big, you might put an embedding layer on top of the one-hot encoding for greater efficiency. sparse representationStoring only the position(s) of nonzero elements in a sparse feature. For example, suppose a categorical feature named You could use a one-hot vector to represent the tree species in each example. A one-hot vector would contain a single
Alternatively, sparse representation would simply identify the position of the particular species. If 24 Notice that the sparse representation is much more compact than the one-hot representation. Click the icon for a slightly more complex example.Suppose each example in your model must represent the words—but not the order of those words—in an English sentence. English consists of about 170,000 words, so English is a categorical feature with about 170,000 elements. Most English sentences use an extremely tiny fraction of those 170,000 words, so the set of words in a single example is almost certainly going to be sparse data. Consider the following sentence: My dog is a great dog You could use a variant of one-hot vector to represent the words in this sentence. In this variant, multiple cells in the vector can contain a nonzero value. Furthermore, in this variant, a cell can contain an integer other than one. Although the words "my", "is", "a", and "great" appear only once in the sentence, the word "dog" appears twice. Using this variant of one-hot vectors to represent the words in this sentence yields the following 170,000-element vector:
A sparse representation of the same sentence would simply be: 0: 1 26100: 2 45770: 1 58906: 1 91520: 1 Click the icon if you are confused.The term "sparse representation" confuses a lot of people because sparse representation is itself not a sparse vector. Rather, sparse representation is actually a dense representation of a sparse vector. The synonym index representation is a little clearer than "sparse representation." sparse vectorA vector whose values are mostly zeroes. See also sparse feature and sparsity. sparsityThe number of elements set to zero (or null) in a vector or matrix divided by the total number of entries in that vector or matrix. For example, consider a 100-element matrix in which 98 cells contain zero. The calculation of sparsity is as follows: $$ {\text{sparsity}} = \frac{\text{98}} {\text{100}} = {\text{0.98}} $$ Feature sparsity refers to the sparsity of a feature vector; model sparsity refers to the sparsity of the model weights. spatial poolingSee pooling. splitIn a decision tree, another name for a condition. splitterWhile training a decision tree, the routine (and algorithm) responsible for finding the best condition at each node. squared hinge lossThe square of the hinge loss. Squared hinge loss penalizes outliers more harshly than regular hinge loss. squared lossSynonym for L2 loss. staged trainingA tactic of training a model in a sequence of discrete stages. The goal can be either to speed up the training process, or to achieve better model quality. An illustration of the progressive stacking approach is shown below:
See also pipelining. stateIn reinforcement learning, the parameter values that describe the current configuration of the environment, which the agent uses to choose an action. state-action value functionSynonym for Q-function. staticSomething done once rather than continuously. The terms static and offline are synonyms. The following are common uses of static and offline in machine learning:
Contrast with dynamic. static inferenceSynonym for offline inference. stationarityA feature whose values don't change across one or more dimensions, usually time. For example, a feature whose values look about the same in 2020 and 2022 exhibits stationarity. In the real world, very few features exhibit stationarity. Even features synonymous with stability (like sea level) change over time. Contrast with nonstationarity. stepA forward pass and backward pass of one batch. See backpropagation for more information on the forward pass and backward pass. step sizeSynonym for learning rate. stochastic gradient descent (SGD)A gradient descent algorithm in which the batch size is one. In other words, SGD trains on a single example chosen uniformly at random from a training set. strideIn a convolutional operation or pooling, the delta in each dimension of the next series of input slices. For example, the following animation demonstrates a (1,1) stride during a convolutional operation. Therefore, the next input slice starts one position to the right of the previous input slice. When the operation reaches the right edge, the next slice is all the way over to the left but one position down.
The preceding example demonstrates a two-dimensional stride. If the input matrix is three-dimensional, the stride would also be three-dimensional. structural risk minimization (SRM)An algorithm that balances two goals:
For example, a function that minimizes loss+regularization on the training set is a structural risk minimization algorithm. Contrast with empirical risk minimization. subsamplingSee pooling. summaryIn TensorFlow, a value or set of values calculated at a particular step, usually used for tracking model metrics during training. supervised machine learningTraining a model from features and their corresponding labels. Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, a student can then provide answers to new (never-before-seen) questions on the same topic. Compare with unsupervised machine learning. synthetic featureA feature not present among the input features, but assembled from one or more of them. Methods for creating synthetic features include the following:
Features created by normalizing or scaling alone are not considered synthetic features. Ttabular Q-learningIn reinforcement learning, implementing Q-learning by using a table to store the Q-functions for every combination of state and action. targetSynonym for label. target networkIn Deep Q-learning, a neural network that is a stable approximation of the main neural network, where the main neural network implements either a Q-function or a policy. Then, you can train the main network on the Q-values predicted by the target network. Therefore, you prevent the feedback loop that occurs when the main network trains on Q-values predicted by itself. By avoiding this feedback, training stability increases. temporal dataData recorded at different points in time. For example, winter coat sales recorded for each day of the year would be temporal data. TensorThe primary data structure in TensorFlow programs. Tensors are N-dimensional (where N could be very large) data structures, most commonly scalars, vectors, or matrices. The elements of a Tensor can hold integer, floating-point, or string values. TensorBoardThe dashboard that displays the summaries saved during the execution of one or more TensorFlow programs. TensorFlowA large-scale, distributed, machine learning platform. The term also refers to the base API layer in the TensorFlow stack, which supports general computation on dataflow graphs. Although TensorFlow is primarily used for machine learning, you may also use TensorFlow for non-ML tasks that require numerical computation using dataflow graphs. TensorFlow PlaygroundA program that visualizes how different hyperparameters influence model (primarily neural network) training. Go to http://playground.tensorflow.org to experiment with TensorFlow Playground. TensorFlow ServingA platform to deploy trained models in production. Tensor Processing Unit (TPU)An application-specific integrated circuit (ASIC) that optimizes the performance of machine learning workloads. These ASICs are deployed as multiple TPU chips on a TPU device. Tensor rankSee rank (Tensor). Tensor shapeThe number of elements a Tensor contains in various dimensions. For example, a [5, 10] Tensor has a shape of 5 in one dimension and 10 in another. Tensor sizeThe total number of scalars a Tensor contains. For example, a [5, 10] Tensor has a size of 50. termination conditionIn reinforcement learning, the conditions that determine when an episode ends, such as when the agent reaches a certain state or exceeds a threshold number of state transitions. For example, in tic-tac-toe (also known as noughts and crosses), an episode terminates either when a player marks three consecutive spaces or when all spaces are marked. testIn a decision tree, another name for a condition. test lossA metric representing a model's loss against the test set. When building a model, you typically try to minimize test loss. That's because a low test loss is a stronger quality signal than a low training loss or low validation loss. A large gap between test loss and training loss or validation loss sometimes suggests that you need to increase the regularization rate. test setA subset of the dataset reserved for testing a trained model. Traditionally, you divide examples in the dataset into the following three distinct subsets:
Each example in a dataset should belong to only one of the preceding subsets. For instance, a single example should not belong to both the training set and the test set. The training set and validation set are both closely tied to training a model. Because the test set is only indirectly associated with training, test loss is a less biased, higher quality metric than training loss or validation loss. tf.ExampleA standard protocol buffer for describing input data for machine learning model training or inference. tf.kerasAn implementation of Keras integrated into TensorFlow. threshold (for decision trees)In an axis-aligned condition, the value that a feature is being compared against. For example, 75 is the threshold value in the following condition: grade >= 75 time series analysisA subfield of machine learning and statistics that analyzes temporal data. Many types of machine learning problems require time series analysis, including classification, clustering, forecasting, and anomaly detection. For example, you could use time series analysis to forecast the future sales of winter coats by month based on historical sales data. timestepOne "unrolled" cell within a recurrent neural network. For example, the following figure shows three timesteps (labeled with the subscripts t-1, t, and t+1):
tokenIn a language model, the atomic unit that the model is training on and making predictions on. A token is typically one of the following:
In domains outside of language models, tokens can represent other kinds of atomic units. For example, in computer vision, a token might be a subset of an image. towerA component of a deep neural network that is itself a deep neural network without an output layer. Typically, each tower reads from an independent data source. Towers are independent until their output is combined in a final layer. TPUAbbreviation for Tensor Processing Unit. TPU chipA programmable linear algebra accelerator with on-chip high bandwidth memory that is optimized for machine learning workloads. Multiple TPU chips are deployed on a TPU device. TPU deviceA printed circuit board (PCB) with multiple TPU chips, high bandwidth network interfaces, and system cooling hardware. TPU masterThe central coordination process running on a host machine that sends and receives data, results, programs, performance, and system health information to the TPU workers. The TPU master also manages the setup and shutdown of TPU devices. TPU nodeA TPU resource on Google Cloud Platform with a specific TPU type. The TPU node connects to your VPC Network from a peer VPC network. TPU nodes are a resource defined in the Cloud TPU API. TPU PodA specific configuration of TPU devices in a Google data center. All of the devices in a TPU pod are connected to one another over a dedicated high-speed network. A TPU Pod is the largest configuration of TPU devices available for a specific TPU version. TPU resourceA TPU entity on Google Cloud Platform that you create, manage, or consume. For example, TPU nodes and TPU types are TPU resources. TPU sliceA TPU slice is a fractional portion of the TPU devices in a TPU Pod. All of the devices in a TPU slice are connected to one another over a dedicated high-speed network. TPU typeA configuration
of one or more TPU devices with a specific TPU hardware version. You select a TPU type when you create a TPU node on Google Cloud Platform. For example, a TPU workerA process that runs on a host machine and executes machine learning programs on TPU devices. trainingThe process of determining the ideal parameters (weights and biases) comprising a model. During training, a system reads in examples and gradually adjusts parameters. Training uses each example anywhere from a few times to billions of times. training lossA metric representing a model's loss during a particular training iteration. For example, suppose the loss function is Mean Squared Error. Perhaps the training loss (the Mean Squared Error) for the 10th iteration is 2.2, and the training loss for the 100th iteration is 1.9. A loss curve plots training loss vs. the number of iterations. A loss curve provides the following hints about training:
For example, the following somewhat idealized loss curve shows:
Although training loss is important, see also generalization. training-serving skewThe difference between a model's performance during training and that same model's performance during serving. training setThe subset of the dataset used to train a model. Traditionally, examples in the dataset are divided into the following three distinct subsets:
Ideally, each example in the dataset should belong to only one of the preceding subsets. For example, a single example should not belong to both the training set and the validation set. trajectoryIn reinforcement learning, a sequence of tuples that represent a sequence of state transitions of the agent, where each tuple corresponds to the state, action, reward, and next state for a given state transition. transfer learningTransferring information from one machine learning task to another. For example, in multi-task learning, a single model solves multiple tasks, such as a deep model that has different output nodes for different tasks. Transfer learning might involve transferring knowledge from the solution of a simpler task to a more complex one, or involve transferring knowledge from a task where there is more data to one where there is less data. Most machine learning systems solve a single task. Transfer learning is a baby step towards artificial intelligence in which a single program can solve multiple tasks. TransformerA neural network architecture developed at Google that relies on self-attention mechanisms to transform a sequence of input embeddings into a sequence of output embeddings without relying on convolutions or recurrent neural networks. A Transformer can be viewed as a stack of self-attention layers. A Transformer can include any of the following:
An encoder transforms a sequence of embeddings into a new sequence of the same length. An encoder includes N identical layers, each of which contains two sub-layers. These two sub-layers are applied at each position of the input embedding sequence, transforming each element of the sequence into a new embedding. The first encoder sub-layer aggregates information from across the input sequence. The second encoder sub-layer transforms the aggregated information into an output embedding. A decoder transforms a sequence of input embeddings into a sequence of output embeddings, possibly with a different length. A decoder also includes N identical layers with three sub-layers, two of which are similar to the encoder sub-layers. The third decoder sub-layer takes the output of the encoder and applies the self-attention mechanism to gather information from it. The blog post Transformer: A Novel Neural Network Architecture for Language Understanding provides a good introduction to Transformers. translational invarianceIn an image classification problem, an algorithm's ability to successfully classify images even when the position of objects within the image changes. For example, the algorithm can still identify a dog, whether it is in the center of the frame or at the left end of the frame. See also size invariance and rotational invariance. trigramAn N-gram in which N=3. true negative (TN)An example in which the model correctly predicts the negative class. For example, the model infers that a particular email message is not spam, and that email message really is not spam. true positive (TP)An example in which the model correctly predicts the positive class. For example, the model infers that a particular email message is spam, and that email message really is spam. true positive rate (TPR)Synonym for recall. That is: $$\text{true positive rate} = \frac{\text{true positives}} {\text{true positives} + \text{false negatives}}$$ True positive rate is the y-axis in an ROC curve. Uunawareness (to a sensitive attribute)A situation in which sensitive attributes are present, but not included in the training data. Because sensitive attributes are often correlated with other attributes of one’s data, a model trained with unawareness about a sensitive attribute could still have disparate impact with respect to that attribute, or violate other fairness constraints. underfittingProducing a model with poor predictive ability because the model hasn't fully captured the complexity of the training data. Many problems can cause underfitting, including:
undersamplingRemoving examples from the majority class in a class-imbalanced dataset in order to create a more balanced training set. For example, consider a dataset in which the ratio of the majority class to the minority class is 20:1. To overcome this class imbalance, you could create a training set consisting of all of the minority class examples but only a tenth of the majority class examples, which would create a training-set class ratio of 2:1. Thanks to undersampling, this more balanced training set might produce a better model. Alternatively, this more balanced training set might contain insufficient examples to train an effective model. Contrast with oversampling. unidirectionalA system that only evaluates the text that precedes a target section of text. In contrast, a bidirectional system evaluates both the text that precedes and follows a target section of text. See bidirectional for more details. unidirectional language modelA language model that bases its probabilities only on the tokens appearing before, not after, the target token(s). Contrast with bidirectional language model. unlabeled exampleAn example that contains features but no label. For example, the following table shows three unlabeled examples from a house valuation model, each with three features but no house value:
In supervised machine learning, models train on labeled examples and make predictions on unlabeled examples. In semi-supervised and unsupervised learning, unlabeled examples are used during training. Contrast unlabeled example with labeled example. unsupervised machine learning#clustering #fundamentals Training a model to find patterns in a dataset, typically an unlabeled dataset. The most common use of unsupervised machine learning is to cluster data into groups of similar examples. For example, an unsupervised machine learning algorithm can cluster songs based on various properties of the music. The resulting clusters can become an input to other machine learning algorithms (for example, to a music recommendation service). Clustering can help when useful labels are scarce or absent. For example, in domains such as anti-abuse and fraud, clusters can help humans better understand the data. Contrast with supervised machine learning. Click the icon for additional notes.Another example of unsupervised machine learning is principal component analysis (PCA). For example, applying PCA on a dataset containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. uplift modelingA modeling technique, commonly used in marketing, that models the "causal effect" (also known as the "incremental impact") of a "treatment" on an "individual." Here are two examples:
Uplift modeling differs from classification or regression in that some labels (for example, half of the labels in binary treatments) are always missing in uplift modeling. For example, a patient can either receive or not receive a treatment; therefore, we can only observe whether the patient is going to heal or not heal in only one of these two situations (but never both). The main advantage of an uplift model is that it can generate predictions for the unobserved situation (the counterfactual) and use it to compute the causal effect. upweightingApplying a weight to the downsampled class equal to the factor by which you downsampled. user matrixIn recommendation systems, an embedding vector generated by matrix factorization that holds latent signals about user preferences. Each row of the user matrix holds information about the relative strength of various latent signals for a single user. For example, consider a movie recommendation system. In this system, the latent signals in the user matrix might represent each user's interest in particular genres, or might be harder-to-interpret signals that involve complex interactions across multiple factors. The user matrix has a column for each latent feature and a row for each user. That is, the user matrix has the same number of rows as the target matrix that is being factorized. For example, given a movie recommendation system for 1,000,000 users, the user matrix will have 1,000,000 rows. VvalidationThe initial evaluation of a model's quality. Validation checks the quality of a model's predictions against the validation set. Because the validation set differs from the training set, validation helps guard against overfitting. You might think of evaluating the model against the validation set as the first round of testing and evaluating the model against the test set as the second round of testing. validation lossA metric representing a model's loss on the validation set during a particular iteration of training. See also generalization curve. validation setThe subset of the dataset that performs initial evaluation against a trained model. Typically, you evaluate the trained model against the validation set several times before evaluating the model against the test set. Traditionally, you divide the examples in the dataset into the following three distinct subsets:
Ideally, each example in the dataset should belong to only one of the preceding subsets. For example, a single example should not belong to both the training set and the validation set. vanishing gradient problemThe tendency for the gradients of early hidden layers of some deep neural networks to become surprisingly flat (low). Increasingly lower gradients result in increasingly smaller changes to the weights on nodes in a deep neural network, leading to little or no learning. Models suffering from the vanishing gradient problem become difficult or impossible to train. Long Short-Term Memory cells address this issue. Compare to exploding gradient problem. variable importancesA set of scores that indicates the relative importance of each feature to the model. For example, consider a decision tree that estimates house prices. Suppose this decision tree uses three features: size, age, and style. If a set of variable importances for the three features are calculated to be {size=5.8, age=2.5, style=4.7}, then size is more important to the decision tree than age or style. Different variable importance metrics exist, which can inform ML experts about different aspects of models. WWasserstein lossOne of the loss functions commonly used in generative adversarial networks, based on the earth mover's distance between the distribution of generated data and real data. weightA value that a model multiplies by another value. Training is the process of determining a model's ideal weights; inference is the process of using those learned weights to make predictions. Click the icon to see an example of weights in a linear model.Imagine a linear model with two features. Suppose that training determines the following weights (and bias):
Now imagine an example with the following feature values:
This linear model uses the following formula to generate a prediction, y': $$y' = b + w_1x_1 + w_2x_2$$ Therefore, the prediction is: $$y' = 2.2 + (1.5)(6) + (0.4)(10) = 15.2$$ If a weight is 0, then the corresponding feature does not contribute to the model. For example, if w1 is 0, then the value of x1 is irrelevant. Weighted Alternating Least Squares (WALS)An algorithm for minimizing the objective function during matrix factorization in recommendation systems, which allows a downweighting of the missing examples. WALS minimizes the weighted squared error between the original matrix and the reconstruction by alternating between fixing the row factorization and column factorization. Each of these optimizations can be solved by least squares convex optimization. For details, see the Recommendation Systems course. weighted sumThe sum of all the relevant input values multiplied by their corresponding weights. For example, suppose the relevant inputs consist of the following:
The weighted sum is therefore: weighted sum = (2)(-1.3) + (-1)(0.6) + (3)(0.4) = -2.0 A weighted sum is the input argument to an activation function. wide modelA linear model that typically has many sparse input features. We refer to it as "wide" since such a model is a special type of neural network with a large number of inputs that connect directly to the output node. Wide models are often easier to debug and inspect than deep models. Although wide models cannot express nonlinearities through hidden layers, wide models can use transformations such as feature crossing and bucketization to model nonlinearities in different ways. Contrast with deep model. widthThe number of neurons in a particular layer of a neural network. wisdom of the crowdThe idea that averaging the opinions or estimates of a large group of people ("the crowd") often produces surprisingly good results. For example, consider a game in which people guess the number of jelly beans packed into a large jar. Although most individual guesses will be inaccurate, the average of all the guesses has been empirically shown to be surprisingly close to the actual number of jelly beans in the jar. Ensembles are a software analog of wisdom of the crowd. Even if individual models make wildly inaccurate predictions, averaging the predictions of many models often generates surprisingly good predictions. For example, although an individual decision tree might make poor predictions, a decision forest often makes very good predictions. word embeddingRepresenting each word in a word set within an embedding vector; that is, representing each word as a vector of floating-point values between 0.0 and 1.0. Words with similar meanings have more-similar representations than words with different meanings. For example, carrots, celery, and cucumbers would all have relatively similar representations, which would be very different from the representations of airplane, sunglasses, and toothpaste. ZZ-score normalizationA scaling technique that replaces a raw feature value with a floating-point value representing the number of standard deviations from that feature's mean. For example, consider a feature whose mean is 800 and whose standard deviation is 100. The following table shows how Z-score normalization would map the raw value to its Z-score:
The machine learning model then trains on the Z-scores for that feature instead of on the raw values. What is the broadcast industry term for syndicated content that originally aired on a network?Three common types of syndication are: first-run syndication, which is programming that is broadcast for the first time as a syndicated show and is made specifically to sell directly into syndication; off-network syndication (colloquially called a "rerun"), which is the licensing of a program whose first airing was on ...
Which of the following technologies was the first to lead audiences to expect more control over their television watching experience quizlet?In the U.S., DBS is dominated by two companies: DirecTV and Dish Network. It is a multichannel service. Which of the following technologies was the first to lead audiences to expect more control over their television-watching experience? interactive cable.
Which of the following programs is an example of firstExamples of first-run syndications include: Wheel of Fortune, Wild Kingdom, Judge Judy (and the other “judge” shows), and Dr. Phil. Off-network or second-run syndication. These shows ran first on a network or major radio group.
Which are reasons for à la carte pricing in cable television?A la carte pricing has been an often-requested but seldom-delivered option for cable and satellite distribution services. In the U.S., proponents have argued that the model would deliver lower prices, while opponents maintain that bundling offers more customer value and program diversity.
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