Google Machine Learning Glossary

holdout data

Examples intentionally not used ("held out") during training. The validation data set and test data set 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 data set than does the loss on the training set.

Sparse Feature

Feature vector whose values are predominately zero or empty. For example, a vector containing a single 1 value and a million 0 values is sparse. As another example, words in a search query could also be a sparse feature—there are many possible words in a given language, but only a few of them occur in a given query. Contrast with dense feature.

Categorical Data

Features having a discrete set of possible values. For example, consider a categorical feature named house style, which has a discrete set of three possible values: Tudor, ranch, colonial. By representing house style as categorical data, the model can learn the separate impacts of Tudor, ranch, and colonial on house price. Sometimes, values in the discrete set are mutually exclusive, and only one value can be applied to a given example. For example, a car maker categorical feature would probably permit only a single value (Toyota) per example. Other times, more than one value may be applicable. A single car could be painted more than one different color, so a car color categorical feature would likely permit a single example to have multiple values (for example, red and white). Categorical features are sometimes called discrete features. Contrast with numerical data.

L1 Loss

Loss function based on the absolute value of the difference between the values that a model is predicting and the actual values of the labels. L1 loss is less sensitive to outliers than L2 loss.

Session

Maintains state (for example, variables) within a TensorFlow program.

Collaborative Filtering

Making predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems.

Numerical Data

Features represented as integers or real-valued numbers. For example, in a real estate model, you 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 each other and possibly to the label. For example, representing the size of a house as numerical data indicates that a 200 square-meter house is twice as large as a 100 square-meter house. Furthermore, the number of square meters in a house probably has some mathematical relationship to the price 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 20000 is not twice (or half) as potent as a postal code of 10000. Furthermore, although different postal codes do correlate to different real estate values, we can't assume that real estate values at postal code 20000 are twice as valuable as real estate values at postal code 10000. Postal codes should be represented as categorical data instead. Numerical features are sometimes called continuous features.

Offline Inference

Generating a group of predictions, storing those predictions, and then retrieving those predictions on demand. Contrast with online inference.

Online Inference

Generating predictions on demand. Contrast with offline inference.

Pre-Trained Model

Models or model components (such as embeddings) that have been already been trained. Sometimes, you'll feed pre-trained embeddings into a neural network. Other times, your model will train the embeddings itself rather than rely on the pre-trained embeddings.

One vs. All

Given a classification problem with N possible solutions, a one-vs.-all solution consists 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: animal vs. not animal vegetable vs. not vegetable mineral vs. not mineral

Layers API

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: tf.layers.Dense for a fully-connected layer. tf.layers.Conv2D for a convolutional layer. When writing a custom Estimator, you compose Layers objects to define the characteristics of all the hidden layers. The Layers API follows the [Keras](#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.

Metrics API

A TensorFlow API for evaluating models. For example, tf.metrics.accuracy determines how often a model's predictions match labels. When writing a custom Estimator, you invoke Metrics API functions to specify how your model should be evaluated.

Queue

A TensorFlow Operation that implements a queue data structure. Typically used in I/O.

Saver

A TensorFlow object responsible for saving model checkpoints.

Class-Imbalanced Data Set

A binary classification problem in which the labels for the two classes have significantly different frequencies. For example, a disease data set in which 0.0001 of examples have positive labels and 0.9999 have negative labels is a class-imbalanced problem, but a football game predictor in which 0.51 of examples label one team winning and 0.49 label the other team winning is not a class-imbalanced problem.

hyperplane

A 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.

Embeddings

A categorical feature represented as a continuous-valued feature. Typically, an embedding is a translation of a high-dimensional vector into a low-dimensional space. For example, you can represent the words in an English sentence in either of the following two ways: As a million-element (high-dimensional) sparse vector in which all elements are integers. Each cell in the vector represents a separate English word; the value in a cell represents the number of times that word appears in a sentence. Since a single English sentence is unlikely to contain more than 50 words, nearly every cell in the vector will contain a 0. The few cells that aren't 0 will contain a low integer (usually 1) representing the number of times that word appeared in the sentence. As a several-hundred-element (low-dimensional) dense vector in which each element holds a floating-point value between 0 and 1. This is an embedding. In TensorFlow, embeddings are trained by backpropagating loss just like any other parameter in a neural network.

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 data set consists of a hundred features. In order to maximize the margin between positive and negative classes, KSVMs could internally map those features into a million-dimension space. KSVMs uses a loss function called hinge loss.

Weight

A coefficient for a feature in a linear model, or an edge in a deep network. The goal of training a linear model is to determine the ideal weight for each feature. If a weight is 0, then its corresponding feature does not contribute to the model.

Data Set

A collection of examples.

Pandas

A column-oriented data analysis API. Many ML frameworks, including TensorFlow, support pandas data structures as input. See pandas documentation.

Graph

In 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.

Scaling

A commonly used practice in feature engineering to tame a feature's range of values to match the range of other features in the data set. For example, suppose that you want all floating-point features in the data set to have a range of 0 to 1. Given a particular feature's range of 0 to 500, you could scale that feature by dividing each value by 500. See also normalization.

ROC (Receiver Operating Characteristic) Curve

A curve of true positive rate vs. false positive rate at different classification thresholds. See also AUC.

Input Function

In TensorFlow, a function that returns input data to the training, evaluation, or prediction method of an Estimator. For example, the training input function returns a batch of features and labels from the training set.

Partial Derivative

A 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.

Summary

In TensorFlow, a value or set of values calculated at a particular step, usually used for tracking model metrics during training.

hinge loss

A 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: loss=max(0,1−(y′∗y)) where y' is the raw output of the classifier model: y′=b+w1x1+w2x2+...wnxn and y is the true label, either -1 or +1. Consequently, a plot of hinge loss vs. (y * y') looks as follows:

Dense Feature

A feature in which most values are non-zero, typically a Tensor of floating-point values. Contrast with sparse feature.

Synthetic Feature

A feature that is not present among the input features, but is derived from one or more of them. Kinds of synthetic features include the following: Multiplying one feature by itself or by other feature(s). (These are termed feature crosses.) Dividing one feature by a second feature. Bucketing a continuous feature into range bins. Features created by normalizing or scaling alone are not considered synthetic features.

Discrete Feature

A 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.

Continuous Feature

A floating-point feature with an infinite range of possible values. Contrast with discrete feature.

Dropout Regularization

A form of regularization useful in training neural networks. Dropout regularization works by removing 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.

Step

A forward and backward evaluation of one batch.

Epoch

A full training pass over the entire data set such that each example has been seen once. Thus, an epoch represents N/batch size training iterations, where N is the total number of examples.

Activation Function

A function (for example, ReLU or sigmoid) that takes in the weighted sum of all of the inputs from the previous layer and then generates and passes an output value (typically nonlinear) to the next layer.

Convex Function

A 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: A typical convex function is shaped like the letter 'U'. By contrast, the following function is not convex. Notice how the region above the graph is not a convex set: local minimum local minimum global minimum 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. A lot of the common loss functions, including the following, are convex functions: L2 loss Log Loss L1 regularization L2 regularization 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.

Sigmoid Function

A function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1. The sigmoid function has the following formula: y=11+e−σ where σ in logistic regression problems is simply: σ=b+w1x1+w2x2+...wnxn In other words, the sigmoid function converts σ into a probability between 0 and 1. In some neural networks, the sigmoid function acts as the activation function.

Softmax

A function that provides probabilities for each possible class in a multi-class classification model. The probabilities add up to exactly 1.0. For example, softmax might determine that the probability of a particular image being a dog at 0.9, a cat at 0.08, and a horse at 0.02. (Also called full softmax.) Contrast with candidate sampling.

Cross-Entropy

A generalization of Log Loss to multi-class classification problems. Cross-entropy quantifies the difference between two probability distributions. See also perplexity.

Negative Class

In binary classification, one class is termed positive and the other is termed negative. The positive class is the thing we're looking for and the negative class is the other possibility. For example, the negative class in a medical test might be "not tumor." The negative class in an email classifier might be "not spam." See also positive class.

Positive Class

In binary classification, the two possible classes are labeled as positive and negative. The positive outcome is the thing we're testing for. (Admittedly, we're simultaneously testing for both outcomes, but play along.) For example, the positive class in a medical test might be "tumor." The positive class in an email classifier might be "spam." Contrast with negative class.

Inference

In machine learning, often refers to the process of making predictions by applying the trained model to unlabeled examples. In statistics, inference refers to the process of fitting the parameters of a distribution conditioned on some observed data. (See the Wikipedia article on statistical inference.)

Label

In supervised learning, the "answer" or "result" portion of an example. Each example in a labeled data set consists of one or more features and a label. For instance, in a housing data set, the features might include the number of bedrooms, the number of bathrooms, and the age of the house, while the label might be the house's price. in a spam detection dataset, the features might include the subject line, the sender, and the email message itself, while the label would probably be either "spam" or "not spam."

Convergence

Informally, often refers to a state reached during training in which training loss and validation loss change very little or not at all with each iteration after a certain number of iterations. In other words, a model reaches convergence when additional training on the current data will not improve the model. In deep learning, loss values sometimes stay constant or nearly so for many iterations before finally descending, temporarily producing a false sense of convergence. See also early stopping. See also Boyd and Vandenberghe, Convex Optimization.

Generalized Linear Model

A 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: logistic regression multi-class regression least squares regression The parameters of a generalized linear model can be found through convex optimization. Generalized linear models exhibit the following properties: The average prediction of the optimal least squares regression model is equal to the average label on the training data. The average probability predicted by the optimal logistic regression model is equal to the average label on the training data. The power of a generalized linear model is limited by its features. Unlike a deep model, a generalized linear model cannot "learn new features."

Stochastic Gradient Descent (SGD)

A gradient descent algorithm in which the batch size is one. In other words, SGD relies on a single example chosen uniformly at random from a data set to calculate an estimate of the gradient at each step.

Mini-Batch Stochastic Gradient Descent (SGD)

A gradient descent algorithm that uses mini-batches. In other words, mini-batch SGD estimates the gradient based on a small subset of the training data. Vanilla SGD uses a mini-batch of size 1.

Fully Connected Layer

A 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.

Data Set API

A high-level TensorFlow API for reading data and transforming it into a form that a machine learning algorithm requires. A tf.data.Dataset object represents a sequence of elements, in which each element contains one or more Tensors. A tf.data.Iterator object provides access to the elements of a Dataset. For details about the Dataset API, see Importing Data in the TensorFlow Programmer's Guide.

Rater

A human who provides labels in examples. Sometimes called an "annotator."

Parameter Server (PS)

A job that keeps track of a model's parameters in a distributed setting.

TensorFlow

A 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.

wide model

A 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, they can use transformations such as feature crossing and bucketization to model nonlinearities in different ways. Contrast with deep model.

Least Squares Regression

A linear regression model trained by minimizing L2 Loss.

Loss

A measure of how far a model's predictions are from its label. Or, to phrase it more pessimistically, a measure of how bad the model is. To determine this value, a model must define a loss function. For example, linear regression models typically use mean squared error for a loss function, while logistic regression models use Log Loss.

Inter-Rater Agreement

A 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.

Ensemble

A merger of the predictions of multiple models. You can create an ensemble via one or more of the following: different initializations different hyperparameters different overall structure Deep and wide models are a kind of ensemble.

Early Stopping

A method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation data set starts to increase, that is, when generalization performance worsens.

Recall

A metric for classification models that answers the following question: Out of all the possible positive labels, how many did the model correctly identify? That is: Recall=True Positives/(True Positives+False Negatives)

Precision

A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. That is: Precision=True Positives/(True Positives+False Positives)

Objective

A metric that your algorithm is trying to optimize.

Logistic Regression

A model that generates a probability for each possible discrete label value in classification problems by applying a sigmoid function to a linear prediction. Although logistic regression is often used in binary classification problems, it can also be used in multi-class classification problems (where it becomes called multi-class logistic regression or multinomial regression).

Static Model

A model that is trained offline.

Dynamic Model

A model that is trained online in a continuously updating fashion. That is, data is continuously entering the model.

Neural Network

A model that, taking inspiration from the brain, is composed of layers (at least one of which is hidden) consisting of simple connected units or neurons followed by nonlinearities.

Sequence Model

A model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos.

Prediction

A model's output when provided with an input example.

Neuron

A node in a neural network, typically taking in multiple input values and generating one output value. The neuron calculates the output value by applying an activation function (nonlinear transformation) to a weighted sum of input values.

Operation (op)

A node in the TensorFlow graph. In TensorFlow, any procedure that creates, manipulates, or destroys a Tensor is an operation. For example, a matrix multiply is an operation that takes two Tensors as input and generates one Tensor as output.

Metric

A number that you care about. May or may not be directly optimized in a machine-learning system. A metric that your system tries to optimize is called an objective.

TensorFlow Serving

A platform to deploy trained models in production.

Keras

A popular Python machine learning API. Keras runs on several deep learning frameworks, including TensorFlow, where it is made available as tf.keras.

Scikit-Learn

A popular open-source ML platform. See www.scikit-learn.org.

Calibration Layer

A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.

heuristic

A practical and nonoptimal solution to a problem, which is sufficient for making progress or for learning from.

Machine Learning

A program or system that builds (trains) a predictive model from input data. The system uses the learned model to 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.

TensorFlow Playground

A program that visualizes how different hyperparameters influence model (primarily neural network) training. Go to http://playground.tensorflow.org to experiment with TensorFlow Playground.

Stationarity

A property of data in a data set, in which the data distribution stays constant across one or more dimensions. Most commonly, that dimension is time, meaning that data exhibiting stationarity doesn't change over time. For example, data that exhibits stationarity doesn't change from September to December.

Learning Rate

A scalar used to train a model via gradient descent. During each iteration, the gradient descent algorithm multiplies the learning rate by the gradient. The resulting product is called the gradient step. Learning rate is a key hyperparameter.

Regularization Rate

A scalar value, represented as lambda, specifying the relative importance of the regularization function. The following simplified loss equation shows the regularization rate's influence: minimize(loss function + λ(regularization function)) Raising the regularization rate reduces overfitting but may make the model less accurate.

Classification Threshold

A scalar-value criterion that is applied to a model's predicted score in order to separate the positive class from the negative class. Used when mapping logistic regression results to binary classification. For example, consider a logistic regression model that determines the probability of a given email message being spam. If the classification threshold is 0.9, then logistic regression values above 0.9 are classified as spam and those below 0.9 are classified as not spam.

Layer

A set of neurons in a neural network that process a set of input features, or the output of those neurons. Also, an abstraction in TensorFlow. Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

Feature Columns

A set of related features, such as the set of all possible countries in which users might live. An example may have one or more features present in a feature column. Feature columns in TensorFlow also encapsulate metadata such as: the feature's data type whether a feature is fixed length or should be converted to an embedding A feature column can contain a single feature. "Feature column" is Google-specific terminology. A feature column is referred to as a "namespace" in the VW system (at Yahoo/Microsoft), or a field.

Baseline

A simple model or heuristic used as reference point for comparing how well a model is performing. A baseline helps model developers quantify the minimal, expected performance on a particular problem.

Iteration

A single update of a model's weights during training. An iteration consists of computing the gradients of the parameters with respect to the loss on a single batch of data.

Mini-Batch

A small, randomly selected subset of the entire batch of examples run together in a single iteration of training or inference. The batch size of a mini-batch is usually between 10 and 1,000. It is much more efficient to calculate the loss on a mini-batch than on the full training data.

Momentum

A 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.

AdaGrad

A 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.

One-hot Encoding

A sparse vector in which: One element is set to 1. All other elements are set to 0. One-hot encoding is commonly used to represent strings or identifiers that have a finite set of possible values. For example, suppose a given botany data set chronicles 15,000 different species, each denoted with a unique string identifier. As part of feature engineering, you'll probably encode those string identifiers as one-hot vectors in which the vector has a size of 15,000.

Optimizer

A specific implementation of the gradient descent algorithm. TensorFlow's base class for optimizers is tf.train.Optimizer. Different optimizers may leverage one or more of the following concepts to enhance the effectiveness of gradient descent on a given training set: momentum (Momentum) update frequency (AdaGrad = ADAptive GRADient descent; Adam = ADAptive with Momentum; RMSProp) sparsity/regularization (Ftrl) more complex math (Proximal, and others) You might even imagine an NN-driven optimizer.

tf. Example

A standard protocol buffer for describing input data for machine learning model training or inference.

A/B testing

A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.

Time Series Analysis

A 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.

Convex Set

A 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: A rectangle and a semi-ellipse are both convex sets. By contrast, the following two shapes are not convex sets: A pie-chart with a missing slice and a firework are both nonconvex sets.

Validation Set

A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. Contrast with training set and test set.

Feature Cross

A synthetic feature formed by crossing (multiplying or taking a Cartesian product of) individual features. Feature crosses help represent nonlinear relationships.

hidden layer

A synthetic layer in a neural network between the input layer (that is, the features) and the output layer (the prediction). A neural network contains one or more hidden layers.

Gradient Descent

A technique to minimize loss by computing the gradients of loss with respect to the model's parameters, conditioned on training data. Informally, gradient descent iteratively adjusts parameters, gradually finding the best combination of weights and bias to minimize loss.

Candidate Sampling

A 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.

Binary Classification

A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either "spam" or "not spam" is a binary classifier.

Classification Model

A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian. Compare with regression model.

Regression Model

A type of model that outputs continuous (typically, floating-point) values. Compare with classification models, which output discrete values, such as "day lily" or "tiger lily."

Deep Model

A type of neural network containing multiple hidden layers. Deep models rely on trainable nonlinearities. Contrast with wide model.

Linear Regression

A type of regression model that outputs a continuous value from a linear combination of input features.

L1 Regularization

A type of regularization that penalizes weights in proportion to the sum of the absolute values of the weights. In models relying on sparse features, L1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0, which removes those features from the model. Contrast with L2 regularization.

L2 Regularization

A 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. (Contrast with L1 regularization.) L2 regularization always improves generalization in linear models.

Prediction Bias

A value indicating how far apart the average of predictions is from the average of labels in the data set.

Parameter

A variable of a model that the ML system trains on its own. For example, weights are parameters whose values the ML system gradually learns through successive training iterations. Contrast with hyperparameter.

Tensor Processing Unit (TPU)

An ASIC (application-specific integrated circuit) that optimizes the performance of TensorFlow programs.

Pre-Made Estimator

An Estimator that someone has already built. TensorFlow provides several pre-made Estimators, including DNNClassifier, DNNRegressor, and LinearClassifier. You may build your own pre-made Estimators by following these instructions.

Custom Estimator

An Estimator that you write yourself by following these directions. Contrast with pre-made Estimators.

Confusion Matrix

An NxN table that summarizes how successful a classification model's predictions were; that is, the correlation between the label and the model's classification. One axis of a confusion matrix is the label that the model predicted, and the other axis is the actual label. N represents the number of classes. In a binary classification problem, N=2. For example, here is a sample confusion matrix for a binary classification problem: Tumor (predicted) Non-Tumor (predicted) Tumor (actual) 18 1 Non-Tumor (actual) 6 452 The preceding confusion matrix shows that of the 19 samples that actually had tumors, the model correctly classified 18 as having tumors (18 true positives), and incorrectly classified 1 as not having a tumor (1 false negative). Similarly, of 458 samples that actually did not have tumors, 452 were correctly classified (452 true negatives) and 6 were incorrectly classified (6 false positives). The confusion matrix for a multi-class classification problem can help you determine mistake patterns. For example, a confusion matrix could reveal that a model trained to recognize handwritten digits tends to mistakenly predict 9 instead of 4, or 1 instead of 7. Confusion matrices contain sufficient information to calculate a variety of performance metrics, including precision and recall.

Rectified Linear Unit (ReLU)

An activation function with the following rules: If input is negative or zero, output is 0. If input is positive, output is equal to input.

Structural Risk Minimization (SRM)

An algorithm that balances two goals: The desire to build the most predictive model (for example, lowest loss). The desire to keep the model as simple as possible (for example, strong regularization). For example, a model function that minimizes loss+regularization on the training set is a structural risk minimization algorithm. For more information, see http://www.svms.org/srm/. Contrast with empirical risk minimization.

AUC (Area under the ROC Curve)

An evaluation metric that considers all possible classification thresholds. The Area Under the ROC curve 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.

True Negative (TN)

An example in which the model correctly predicted the negative class. For example, the model inferred that a particular email message was not spam, and that email message really was not spam.

True Positive (TP)

An example in which the model correctly predicted the positive class. For example, the model inferred that a particular email message was spam, and that email message really was spam.

False Negative (FN)

An example in which the model mistakenly predicted the negative class. For example, the model inferred that a particular email message was not spam (the negative class), but that email message actually was spam.

False Positive (FP)

An example in which the model mistakenly predicted the positive class. For example, the model inferred that a particular email message was spam (the positive class), but that email message was actually not spam.

Unlabeled Example

An example that contains features but no label. Unlabeled examples are the input to inference. In semi-supervised and unsupervised learning, unlabeled examples are used during training.

Feature

An input variable used in making predictions.

Estimator

An instance of the tf.Estimator class, which encapsulates logic that builds a TensorFlow graph and runs a TensorFlow session. You may create your own custom Estimators (as described here) or instantiate pre-made Estimators created by others.

Bias

An intercept or offset from an origin. Bias (also known as the bias term) is referred to as b or w0 in machine learning models. For example, bias is the b in the following formula: y′=b+w1x1+w2x2+...wnxn Not to be confused with prediction bias.

Numpy

An open-source math library that provides efficient array operations in Python. pandas is built on numpy.

Node

An overloaded term that means either of the following: A neuron in a hidden layer. An operation in a TensorFlow graph.

Gradient Clipping

Capping gradient values before applying them. Gradient clipping helps ensure numerical stability and prevents exploding gradients.

Empirical Risk Minimization (ERM)

Choosing the model function that minimizes loss on the training set. Contrast with structural risk minimization.

Multi-Class Classification

Classification problems that distinguish among more than two classes. For example, there are approximately 128 species of maple trees, so a model that categorized maple tree species would be multi-class. Conversely, a model that divided emails into only two categories (spam and not spam) would be a binary classification model.

Bucketing

Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range. For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete bins. Given temperature data sensitive to a tenth of a degree, all temperatures between 0.0 and 15.0 degrees could be put into one bin, 15.1 to 30.0 degrees could be a second bin, and 30.1 to 50.0 degrees could be a third bin.

Overfitting

Creating a model that matches the training data so closely that the model fails to make correct predictions on new data.

independently and identically distributed (IID)

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.

Temporal Data

Data recorded at different points in time. For example, winter coat sales recorded for each day of the year would be temporal data.

Checkpoint

Data that captures the state of the variables of a model at a particular time. Checkpoints enable exporting model weights, as well as performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). Note that the graph itself is not included in a checkpoint.

Feature Spec

Describes 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: the data to extract (that is, the keys for the features) the data type (for example, float or int) The length (fixed or variable) The Estimator API provides facilities for producing a feature spec from a list of FeatureColumns.

Perplexity

One 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:

Class

One of a set of enumerated target values for a label. For example, in a binary classification model that detects spam, the two classes are spam and not spam. In a multi-class classification model that identifies dog breeds, the classes would be poodle, beagle, pug, and so on.

Example

One row of a data set. An example contains one or more features and possibly a label. See also labeled example and unlabeled example.

Rank

Overloaded term in ML that can mean either of the following: 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. The ordinal position of a class in an ML 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).

Performance

Overloaded term with the following meanings: The traditional meaning within software engineering. Namely: How fast (or efficiently) does this piece of software run? The meaning within ML. Here, performance answers the following question: How correct is this model? That is, how good are the model's predictions?

Generalization

Refers to your model's ability to make correct predictions on new, previously unseen data as opposed to the data used to train the model.

Binning

See bucketing.

Tensor Rank

See rank.

Full Softmax

See softmax. Contrast with candidate sampling.

L2 Loss

See squared loss.

Instance

Synonym for example.

Dense Layer

Synonym for fully connected layer.

Target

Synonym for label.

Step Size

Synonym for learning rate.

Cost

Synonym for loss.

Multinomial Classification

Synonym for multi-class classification.

True Positive Rate (TP Rate)

Synonym for recall. That is: True Positive Rate=True Positives/(True Positives+False Negatives) True positive rate is the y-axis in an ROC curve.

Lambda

Synonym for regularization rate. (This is an overloaded term. Here we're focusing on the term's definition within regularization.)

Derived Feature

Synonym for synthetic feature.

Output Layer

The "final" layer of a neural network. The layer containing the answer(s).

hyperparameter

The "knobs" that you tweak during successive runs of training a model. For example, learning rate is a hyperparameter. Contrast with parameter.

Partitioning Strategy

The algorithm by which variables are divided across parameter servers.

Mean Squared Error (MSE)

The average squared loss per example. MSE is calculated by dividing the squared loss by the number of examples. The values that TensorFlow Playground displays for "Training loss" and "Test loss" are MSE.

TensorBoard

The dashboard that displays the summaries saved during the execution of one or more TensorFlow programs.

Interpretability

The degree to which a model's predictions can be readily explained. Deep models are often non-interpretable; that is, a deep model's different layers can be hard to decipher. By contrast, linear regression models and wide models are typically far more interpretable.

Root Directory

The directory you specify for hosting subdirectories of the TensorFlow checkpoint and events files of multiple models.

Input Layer

The first layer (the one that receives the input data) in a neural network.

Accuracy

The fraction of predictions that a classification model got right. In multi-class classification, accuracy is defined as follows: Accuracy=Correct PredictionsTotal Number Of Examples In binary classification, accuracy has the following definition: Accuracy=(True Positives+True Negatives)/Total Number Of Examples See true positive and true negative.

Feature Set

The 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.

Pipeline

The 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.

Log Loss

The loss function used in binary logistic regression.

Squared Loss

The loss function used in linear regression. (Also known as L2 Loss.) This function calculates the squares of the difference between a model's predicted value for a labeled example and the actual value of the label. Due to squaring, this loss function amplifies the influence of bad predictions. That is, squared loss reacts more strongly to outliers than L1 loss.

Tensor Shape

The 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.

Batch Size

The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes.

Parameter Update

The operation of adjusting a model's parameters during training, typically within a single iteration of gradient descent.

Regularization

The penalty on a model's complexity. Regularization helps prevent overfitting. Different kinds of regularization include: L1 regularization L2 regularization dropout regularization early stopping (this is not a formal regularization method, but can effectively limit overfitting)

Backpropagation

The primary algorithm for performing gradient descent on neural networks. First, the output values of each node are calculated (and cached) in a forward pass. Then, the partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph.

Tensor

The 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.

Normalization

The process of converting an actual range of values into a standard range of values, typically -1 to +1 or 0 to 1. For example, suppose the natural range of a certain feature is 800 to 6,000. Through subtraction and division, you can normalize those values into the range -1 to +1. See also scaling.

Model Training

The process of determining the best model.

Training

The process of determining the ideal parameters comprising a model.

Feature Engineering

The process of determining which features might be useful in training a model, and then converting raw data from log files and other sources into said features. In TensorFlow, feature engineering often means converting raw log file entries to tf.Example protocol buffers. See also tf.Transform. Feature engineering is sometimes called feature extraction.

Representation

The process of mapping data to useful features.

Convex Optimization

The 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.

SavedModel

The 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 Saving and Restoring in the TensorFlow Programmer's Guide for complete details.

Model

The representation of what an ML system has learned from the training data. This is an overloaded term, which can have either of the following two related meanings: The TensorFlow graph that expresses the structure of how a prediction will be computed. The particular weights and biases of that TensorFlow graph, which are determined by training.

Decision Boundary

The 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:

Batch

The set of examples used in one iteration (that is, one gradient update) of model training. See also batch size.

Squared Hinge Loss

The square of the hinge loss. Squared hinge loss penalizes outliers more harshly than regular hinge loss.

Root Mean Squared Error (RMSE)

The square root of the Mean Squared Error.

Test Set

The subset of the data set that you use to test your model after the model has gone through initial vetting by the validation set. Contrast with training set and validation set.

Training Set

The subset of the data set used to train a model. Contrast with validation set and test set.

Tensor Size

The total number of scalars a Tensor contains. For example, a [5, 10] Tensor has a size of 50.

Gradient

The vector of partial derivatives with respect to all of the independent variables. In machine learning, the gradient is the the vector of partial derivatives of the model function. The gradient points in the direction of steepest ascent.

False Positive Rate (FP rate)

The x-axis in an ROC curve. The FP rate is defined as follows: False Positive Rate=False Positives/(False Positives+True Negatives)

Supervised Machine Learning

Training a model from input data and its corresponding labels. Supervised machine learning is analogous to a student learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, the student can then provide answers to new (never-before-seen) questions on the same topic. Compare with unsupervised machine learning.

Semi-Supervised Learning

Training 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.

Unsupervised Machine Learning

Training a model to find patterns in a data set, typically an unlabeled data set. 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 together 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 be helpful in domains where true labels are hard to obtain. For example, in domains such as anti-abuse and fraud, clusters can help humans better understand the data. Another example of unsupervised machine learning is principal component analysis (PCA). For example, applying PCA on a data set containing the contents of millions of shopping carts might reveal that shopping carts containing lemons frequently also contain antacids. Compare with supervised machine learning.

Transfer Learning

Transferring 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.

Outliers

Values distant from most other values. In machine learning, any of the following are outliers: Weights with high absolute values. Predicted values relatively far away from the actual values. Input data whose values are more than roughly 3 standard deviations from the mean. Outliers often cause problems in model training.

Prior Belief

What 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.

NaN Trap

When 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."