Machine learning terms

See also: Terms and Machine learning

• Fundamental Terms
• Language Evaluation
• Image Models
• Sequence Models
• Reinforcement Learning
• Clustering
• Decision Tree
• Recommendation Systems
• Fairness
• TensorFlow
• Google Cloud
• All Terms

Fundamentals

See also: Machine learning terms/Fundamentals

• accuracy
• activation function
• artificial intelligence
• AUC (Area Under the Curve)
• backpropagation
• batch
• batch size
• bias
• bias (ethics/fairness)
• binary classification
• bucketing
• categorical data
• class
• classification model
• classification threshold
• class-imbalanced dataset
• clipping
• confusion matrix
• continuous feature
• convergence
• DataFrame
• dataset
• deep model
• dense feature
• depth
• discrete feature
• dynamic
• dynamic model
• early stopping
• embedding layer
• epoch
• example
• false negative (FN)
• false positive (FP)
• false positive rate (FPR)
• feature
• feature cross
• feature engineering
• feature set
• feature vector
• feedback loop
• generalization
• generalization curve
• gradient descent
• ground truth
• hidden layer
• hyperparameter
• independently and identically distributed (i.i.d.)
• inference
• input layer
• interpretability
• iteration
• L0 regularization
• L1 loss
• L1 regularization
• L2 loss
• L2 regularization
• label
• labeled example
• lambda
• layer
• learning rate
• linear model
• linear
• linear regression
• logistic regression
• Log Loss
• log-odds
• loss
• loss curve
• loss function
• machine learning
• majority class
• mini-batch
• minority class
• model
• multi-class classification
• negative class
• neural network
• neuron
• node (neural network)
• nonlinear
• nonstationarity
• normalization
• numerical data
• offline
• offline inference
• one-hot encoding
• one-vs.-all
• online inference
• online learning
• output layer
• overfitting
• pandas
• parameter
• positive class
• post-processing
• prediction
• proxy labels
• rater
• Rectified Linear Unit (ReLU)
• regression model
• regularization
• regularization rate
• ReLU
• ROC (receiver operating characteristic) Curve
• Root Mean Squared Error (RMSE)
• sigmoid function
• softmax
• sparse feature
• sparse representation
• sparse vector
• squared loss
• stability
• static
• static inference
• stationarity
• stochastic gradient descent (SGD)
• supervised machine learning
• synthetic feature
• test loss
• training
• training loss
• training-serving skew
• training set
• true negative (TN)
• true positive (TP)
• true positive rate (TPR)
• underfitting
• unlabeled example
• unsupervised machine learning
• validation
• validation loss
• validation set
• weight
• weighted sum
• Z-score normalization

All

See also: Machine learning terms/All

• A/B testing
• accuracy
• action
• activation function
• active learning
• AdaGrad
• agent
• agglomerative clustering
• anomaly detection
• AR
• area under the PR curve
• area under the ROC curve
• artificial general intelligence
• artificial intelligence
• attention
• attribute
• attribute sampling
• AUC (Area under the ROC curve)
• augmented reality
• automation bias
• average precision
• axis-aligned condition
• backpropagation
• bagging
• bag of words
• baseline
• batch
• batch normalization
• batch size
• Bayesian neural network
• Bayesian optimization
• Bellman equation
• BERT (Bidirectional Encoder Representations from Transformers)
• bias (ethics/fairness)
• bias (math) or bias term
• bigram
• bidirectional
• bidirectional language model
• binary classification
• binary condition
• binning
• BLEU (Bilingual Evaluation Understudy)
• boosting
• bounding box
• broadcasting
• bucketing
• calibration layer
• candidate generation
• candidate sampling
• categorical data
• causal language model
• centroid
• centroid-based clustering
• checkpoint
• class
• classification model
• classification threshold
• class-imbalanced dataset
• clipping
• Cloud TPU
• clustering
• co-adaptation
• collaborative filtering
• condition
• confirmation bias
• confusion matrix
• continuous feature
• convenience sampling
• convergence
• convex function
• convex optimization
• convex set
• convolution
• convolutional filter
• convolutional layer
• convolutional neural network
• convolutional operation
• cost
• co-training
• counterfactual fairness
• coverage bias
• crash blossom
• critic
• cross-entropy
• cross-validation
• data analysis
• data augmentation
• DataFrame
• data parallelism
• data set or dataset
• Dataset API (tf.data)
• decision boundary
• decision forest
• decision threshold
• decision tree
• deep model
• decoder
• deep neural network
• Deep Q-Network (DQN)
• demographic parity
• denoising
• dense feature
• dense layer
• depth
• depthwise separable convolutional neural network (sepCNN)
• derived label
• device
• dimension reduction
• dimensions
• discrete feature
• discriminative model
• discriminator
• disparate impact
• disparate treatment
• divisive clustering
• downsampling
• DQN
• dropout regularization
• dynamic
• dynamic model
• eager execution
• early stopping
• earth mover's distance (EMD)
• embedding layer
• embedding space
• embedding vector
• empirical risk minimization (ERM)
• encoder
• ensemble
• entropy
• environment
• episode
• epoch
• epsilon greedy policy
• equality of opportunity
• equalized odds
• Estimator
• example
• experience replay
• experimenter's bias
• exploding gradient problem
• fairness constraint
• fairness metric
• false negative (FN)
• false negative rate
• false positive (FP)
• false positive rate (FPR)
• feature
• feature cross
• feature engineering
• feature extraction
• feature importances
• feature set
• feature spec
• feature vector
• federated learning
• feedback loop
• feedforward neural network (FFN)
• few-shot learning
• fine tuning
• forget gate
• full softmax
• fully connected layer
• GAN
• generalization
• generalization curve
• generalized linear model
• generative adversarial network (GAN)
• generative model
• generator
• GPT (Generative Pre-trained Transformer)
• gini impurity
• gradient
• gradient boosting
• gradient boosted (decision) trees (GBT)
• gradient clipping
• gradient descent
• graph
• graph execution
• greedy policy
• ground truth
• group attribution bias
• hallucination
• hashing
• heuristic
• hidden layer
• hierarchical clustering
• hinge loss
• holdout data
• hyperparameter
• hyperplane
• i.i.d.
• image recognition
• imbalanced dataset
• implicit bias
• incompatibility of fairness metrics
• independently and identically distributed (i.i.d)
• individual fairness
• inference
• inference path
• information gain
• in-group bias
• input layer
• in-set condition
• instance
• interpretability
• inter-rater agreement
• intersection over union (IoU)
• IoU
• item matrix
• items
• iteration
• Keras
• keypoints
• Kernel Support Vector Machines (KSVMs)
• k-means
• k-median
• L0 regularization
• L1 loss
• L1 regularization
• L2 loss
• L2 regularization
• label
• labeled example
• LaMDA (Language Model for Dialogue Applications)
• lambda
• landmarks
• language model
• large language model
• layer
• Layers API (tf.layers)
• leaf
• learning rate
• least squares regression
• linear model
• linear
• linear regression
• logistic regression
• logits
• Log Loss
• log-odds
• Long Short-Term Memory (LSTM)
• loss
• loss curve
• loss function
• loss surface
• LSTM
• machine learning
• majority class
• Markov decision process (MDP)
• Markov property
• masked language model
• matplotlib
• matrix factorization
• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• metric
• meta-learning
• Metrics API (tf.metrics)
• mini-batch
• mini-batch stochastic gradient descent
• minimax loss
• minority class
• ML
• MNIST
• modality
• model
• model capacity
• model parallelism
• model training
• Momentum
• multi-class classification
• multi-class logistic regression
• multi-head self-attention
• multimodal model
• multinomial classification
• multinomial regression
• NaN trap
• natural language understanding
• negative class
• neural network
• neuron
• N-gram
• NLU
• node (neural network)
• node (TensorFlow graph)
• node (decision tree)
• noise
• non-binary condition
• nonlinear
• non-response bias
• nonstationarity
• normalization
• novelty detection
• numerical data
• NumPy
• objective
• objective function
• oblique condition
• offline
• offline inference
• one-hot encoding
• one-shot learning
• one-vs.-all
• online
• online inference
• operation (op)
• out-of-bag evaluation (OOB evaluation)
• optimizer
• out-group homogeneity bias
• outlier detection
• outliers
• output layer
• overfitting
• oversampling
• pandas
• parameter
• Parameter Server (PS)
• parameter update
• partial derivative
• participation bias
• partitioning strategy
• perceptron
• performance
• permutation variable importances
• perplexity
• pipeline
• pipelining
• policy
• pooling
• positive class
• post-processing
• PR AUC (area under the PR curve)
• precision
• precision-recall curve
• prediction
• prediction bias
• predictive parity
• predictive rate parity
• preprocessing
• pre-trained model
• prior belief
• probabilistic regression model
• proxy (sensitive attributes)
• proxy labels
• Q-function
• Q-learning
• quantile
• quantile bucketing
• quantization
• queue
• random forest
• random policy
• ranking
• rank (ordinality)
• rank (Tensor)
• rater
• recall
• recommendation system
• Rectified Linear Unit (ReLU)
• recurrent neural network
• regression model
• regularization
• regularization rate
• reinforcement learning (RL)
• ReLU
• replay buffer
• reporting bias
• representation
• re-ranking
• return
• reward
• ridge regularization
• RNN
• ROC (receiver operating characteristic) Curve
• root
• root directory
• Root Mean Squared Error (RMSE)
• rotational invariance
• sampling bias
• sampling with replacement
• SavedModel
• Saver
• scalar
• scaling
• scikit-learn
• scoring
• selection bias
• self-attention (also called self-attention layer)
• self-supervised learning
• self-training
• semi-supervised learning
• sensitive attribute
• sentiment analysis
• sequence model
• sequence-to-sequence task
• serving
• shape (Tensor)
• shrinkage
• sigmoid function
• similarity measure
• size invariance
• sketching
• softmax
• sparse feature
• sparse representation
• sparse vector
• sparsity
• spatial pooling
• split
• splitter
• squared hinge loss
• squared loss
• stability
• staged training
• state
• state-action value function
• static
• static inference
• stationarity
• step
• step size
• stochastic gradient descent (SGD)
• stride
• structural risk minimization (SRM)
• subsampling
• summary
• supervised machine learning
• synthetic feature
• tabular Q-learning
• target
• target network
• temporal data
• Tensor
• TensorBoard
• TensorFlow
• TensorFlow Playground
• TensorFlow Serving
• Tensor Processing Unit (TPU)
• Tensor rank
• Tensor shape
• Tensor size
• termination condition
• test
• test loss
• test set
• tf.Example
• tf.keras
• threshold (for decision trees)
• time series analysis
• timestep
• token
• tower
• TPU
• TPU chip
• TPU device
• TPU master
• TPU node
• TPU Pod
• TPU resource
• TPU slice
• TPU type
• TPU worker
• training
• training loss
• training-serving skew
• training set
• trajectory
• transfer learning
• Transformer
• translational invariance
• trigram
• true negative (TN)
• true positive (TP)
• true positive rate (TPR)
• unawareness (to a sensitive attribute)
• underfitting
• undersampling
• unidirectional
• unidirectional language model
• unlabeled example
• unsupervised machine learning
• uplift modeling
• upweighting
• user matrix
• validation
• validation loss
• validation set
• vanishing gradient problem
• variable importances
• Wasserstein loss
• weight
• Weighted Alternating Least Squares (WALS)
• weighted sum
• wide model
• width
• wisdom of the crowd
• word embedding
• Z-score normalization