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This glossary defines general machine learning terms as well as terms specific to TensorFlow. Below is a small selection of the most popular entries. You can access this glossary here. For other related glossaries, follow this link.

  • A/B testing
  • activation function
  • agglomerative clustering
  • artificial general intelligence
  • AUC (Area under the ROC Curve)
  • backpropagation
  • bag of words
  • Bayesian neural network
  • Bellman equation

  • binning
  • boosting
  • centroid-based clustering
  • class-imbalanced dataset
  • clustering
  • collaborative filtering
  • confusion matrix
  • convenience sampling
  • convex optimization
  • convolutional filter
  • convolutional neural network
  • cross-entropy
  • cross-validation
  • data augmentation
  • Dataset API (tf.data)
  • decision boundary
  • decision tree
  • deep neural network
  • Deep Q-Network (DQN)
  • dimension reduction
  • discriminative model
  • ensemble
  • false positive (FP)
  • feature engineering
  • feature extraction
  • federated learning
  • feedforward neural network (FFN)
  • generalization curve

  • generalized linear model
  • generative adversarial network (GAN)
  • gradient descent
  • hashing
  • hidden layer
  • hierarchical clustering
  • hinge loss
  • hyperparameter
  • input layer
  • Keras
  • Kernel Support Vector Machines (KSVMs)
  • k-means

  • k-median
  • L1 loss
  • L1 regularization
  • learning rate
  • least squares regression
  • linear model
  • linear regression
  • logistic regression
  • loss curve
  • Markov decision process (MDP)
  • Markov property
  • matrix factorization
  • Mean Squared Error (MSE)
  • minimax loss
  • model training
  • multinomial classification
  • natural language understanding
  • neural network
  • N-gram
  • noise
  • normalization
  • NumPy
  • objective function
  • outliers
  • overfitting
  • pandas
  • perceptron

  • prediction bias
  • pre-trained model
  • prior belief
  • proxy labels
  • Q-function
  • quantile
  • quantile bucketing
  • random forest
  • recommendation system
  • recurrent neural network

  • regression model
  • regularization
  • reinforcement learning (RL)
  • ridge regularization
  • sampling bias
  • scaling
  • scikit-learn
  • scoring
  • selection bias
  • semi-supervised learning
  • sentiment analysis
  • sigmoid function
  • similarity measure
  • size invariance
  • sparse feature
  • stationarity
  • stochastic gradient descent (SGD)
  • structural risk minimization (SRM)
  • subsampling
  • supervised machine learning
  • synthetic feature
  • TensorFlow
  • time series analysis
  • training set
  • transfer learning
  • true positive (TP)
  • underfitting
  • unsupervised machine learning
  • validation
  • vanishing gradient problem
  • Wasserstein loss
  • Weighted Alternating Least Squares (WALS)

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