ML glossary

gistfile1.txt

A/B testing, accuracy, action, activation function, active learning, AdaGrad, agent, agglomerative clustering, AR, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, attribute, AUC (Area under the ROC Curve), augmented reality, automation bias, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian neural network, Bellman equation, bias (ethics/fairness), bias (math), bigram, binary classification, binning, boosting, bounding box, broadcasting, bucketing, calibration layer, candidate generation, candidate sampling, categorical data, centroid, centroid-based clustering, checkpoint, class, classification model, classification threshold, class-imbalanced dataset, clipping, Cloud TPU, clustering, co-adaptation, collaborative filtering, 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, counterfactual fairness, coverage bias, crash blossom, critic, cross-entropy, cross-validation, custom Estimator, data analysis, data augmentation, DataFrame, data set or dataset, Dataset API (tf.data), decision boundary, decision threshold, decision tree, deep model, deep neural network, Deep Q-Network (DQN), demographic parity, dense feature, dense layer, depth, depthwise separable, device, dimension reduction, dimensions, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, downsampling, DQN, dropout regularization, dynamic model, eager execution, early stopping, embeddings, embedding space, empirical risk minimization (ERM), ensemble, 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 positive (FP), false positive rate (FPR), feature, Feature column (tf.feature_column), feature cross, feature engineering, feature extraction, 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, gradient, gradient clipping, gradient descent, graph, graph execution, greedy policy, ground truth, group attribution bias, 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, individual fairness, inference, in-group bias, input function, input layer, 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, L1 loss, L1 regularization, L2 loss, L2 regularization, label, labeled example, lambda, landmarks, layer, Layers API (tf.layers), learning rate, least squares regression, linear model, linear regression, logistic regression, logits, Log Loss, log-odds, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, machine learning, majority class, Markov decision process (MDP), Markov property, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), metric, Metrics API (tf.metrics), mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model capacity, model function, model training, Momentum, multi-class classification, multi-class logistic regression, multinomial classification, NaN trap, natural language understanding, negative class, neural network, neuron, N-gram, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, objective, objective function, offline inference, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), optimizer, out-group homogeneity bias, outliers, output layer, overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participation bias, partitioning strategy, perceptron, performance, perplexity, pipeline, 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, premade Estimator, preprocessing, pre-trained model, prior belief, proxy (sensitive attributes), proxy labels, Q-function, Q-learning, quantile, quantile bucketing, quantization, queue, random forest, random policy, rank (ordinality), rank (Tensor), rater, recall, recommendation system, Rectified Linear Unit (ReLU), recurrent neural network, regression model, regularization, regularization rate, reinforcement learning (RL), replay buffer, reporting bias, representation, re-ranking, return, reward, ridge regularization, RNN, ROC (receiver operating characteristic) Curve, root directory, Root Mean Squared Error (RMSE), rotational invariance, sampling bias, SavedModel, Saver, scalar, scaling, scikit-learn, scoring, selection bias, semi-supervised learning, sensitive attribute, sentiment analysis, sequence model, serving, session (tf.session), shape (Tensor), sigmoid function, similarity measure, size invariance, sketching, softmax, sparse feature, sparse representation, sparse vector, sparsity, spatial pooling, squared hinge loss, squared loss, state, state-action value function, static model, 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 set, tf.Example, tf.keras, time series analysis, timestep, tower, TPU, TPU chip, TPU device, TPU master, TPU node, TPU Pod, TPU resource, TPU slice, TPU type, TPU worker, training, training set, trajectory, transfer learning, translational invariance, trigram, true negative (TN), true positive (TP), true positive rate (TPR), unawareness (to a sensitive attribute), underfitting, unlabeled example, unsupervised machine learning, upweighting, user matrix, validation, validation set, vanishing gradient problem, Wasserstein loss, weight, Weighted Alternating Least Squares (WALS), wide model, width

A/B testing
accuracy
action
activation function
active learning
AdaGrad
agent
agglomerative clustering
AR
area under the PR curve
area under the ROC curve
artificial general intelligence
artificial intelligence
attribute
AUC (Area under the ROC Curve)
augmented reality
automation bias 
average precision 
backpropagation
bag of words
baseline
batch
batch normalization
batch size
Bayesian neural network
Bellman equation
bias (ethics/fairness)
bias (math)
bigram
binary classification
binning
boosting
bounding box
broadcasting
bucketing
calibration layer
candidate generation
candidate sampling
categorical data
centroid
centroid-based clustering
checkpoint
class
classification model
classification threshold
class-imbalanced dataset
clipping
Cloud TPU 
clustering
co-adaptation
collaborative filtering
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
counterfactual fairness 
coverage bias 
crash blossom
critic
cross-entropy
cross-validation
custom Estimator
data analysis
data augmentation
DataFrame
data set or dataset
Dataset API (tf.data)
decision boundary
decision threshold
decision tree
deep model
deep neural network
Deep Q-Network (DQN)
demographic parity
dense feature
dense layer
depth
depthwise separable 
device
dimension reduction
dimensions 
discrete feature
discriminative model
discriminator
disparate impact
disparate treatment
divisive clustering
downsampling
DQN
dropout regularization
dynamic model
eager execution
early stopping
embeddings
embedding space
empirical risk minimization (ERM)
ensemble
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 positive (FP)
false positive rate (FPR)
feature
Feature column (tf.feature_column)
feature cross
feature engineering
feature extraction
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
gradient
gradient clipping
gradient descent
graph
graph execution
greedy policy
ground truth
group attribution bias 
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 
individual fairness
inference
in-group bias 
input function
input layer
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
L1 loss
L1 regularization
L2 loss
L2 regularization
label
labeled example
lambda
landmarks
layer
Layers API (tf.layers)
learning rate
least squares regression
linear model
linear regression
logistic regression
logits
Log Loss
log-odds
Long Short-Term Memory (LSTM)
loss
loss curve
loss surface
LSTM
machine learning
majority class
Markov decision process (MDP)
Markov property
matplotlib
matrix factorization
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
metric
Metrics API (tf.metrics)
mini-batch
mini-batch stochastic gradient descent (SGD)
minimax loss
minority class
ML
MNIST
model
model capacity
model function
model training
Momentum
multi-class classification
multi-class logistic regression
multinomial classification
NaN trap
natural language understanding
negative class
neural network
neuron
N-gram
NLU
node (neural network)
node (TensorFlow graph)
noise
non-response bias 
normalization
numerical data
NumPy
objective
objective function
offline inference
one-hot encoding
one-shot learning
one-vs.-all
online inference
Operation (op)
optimizer
out-group homogeneity bias 
outliers
output layer
overfitting
pandas
parameter
Parameter Server (PS)
parameter update
partial derivative
participation bias
partitioning strategy
perceptron
performance
perplexity
pipeline
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
premade Estimator
preprocessing
pre-trained model
prior belief
proxy (sensitive attributes)
proxy labels
Q-function
Q-learning
quantile
quantile bucketing
quantization
queue
random forest
random policy
rank (ordinality)
rank (Tensor)
rater
recall
recommendation system
Rectified Linear Unit (ReLU)
recurrent neural network 
regression model
regularization
regularization rate
reinforcement learning (RL)
replay buffer
reporting bias 
representation
re-ranking
return
reward
ridge regularization
RNN
ROC (receiver operating characteristic) Curve</
root directory
Root Mean Squared Error (RMSE)
rotational invariance
sampling bias 
SavedModel
Saver
scalar
scaling
scikit-learn
scoring
selection bias 
semi-supervised learning
sensitive attribute
sentiment analysis
sequence model
serving
session (tf.session)
shape (Tensor)
sigmoid function
similarity measure
size invariance
sketching
softmax
sparse feature
sparse representation
sparse vector
sparsity
spatial pooling
squared hinge loss
squared loss
state
state-action value function
static model
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 set
tf.Example
tf.keras
time series analysis
timestep
tower
TPU 
TPU chip 
TPU device 
TPU master
TPU node 
TPU Pod 
TPU resource
TPU slice
TPU type 
TPU worker
training
training set
trajectory
transfer learning
translational invariance
trigram
true negative (TN)
true positive (TP)
true positive rate (TPR)
unawareness (to a sensitive attribute)
underfitting
unlabeled example
unsupervised machine learning
upweighting
user matrix
validation
validation set
vanishing gradient problem
Wasserstein loss
weight
Weighted Alternating Least Squares (WALS)
wide model
width