
Evaluation Metrics
Evaluation metrics are used to measure the quality of the statistical or machine learning model.
Machine Learning Confusion Matrixread it

Generative Adversarial Network
A generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.
Classifier Estimator (Statistics) Autoencoderread it

Active Learning
Active learning is a form of semisupervised machine learning where the algorithm chooses which data to learn from and queries a teacher for guidance.
Natural Language Processing Supervised Learning Machine Learningread it

Convolutional Neural Network
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.
ImageNet Classifier Estimator (Statistics)read it

Batch Normalization
Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.
Supervised Learning Deep Learning Loss Functionread it

Attention Models
Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Vector Neural Network Computer Visionread it

Bayes Theorem
Bayes’ theorem is a formula that governs how to assign a subjective degree of belief to a hypothesis and rationally update that probability with new evidence. Mathematically, it's the the likelihood of event B occurring given that A is true.
Machine Learning Odds (Probability) Prior Probabilityread it

Posterior Probability
In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
Machine Learning Bayesian Inference Bayes Theoremread it

Disentangled Representation Learning
Disentangled representation is an unsupervised learning technique that breaks down, or disentangles, each feature into separate, lower dimension variables.
Distributed Representations Unsupervised Learning Neuronsread it

Deep Belief Network
Deep Belief Networks (DBNs) are a laddering of individual unsupervised networks that use each network’s hidden layer as the input for the next layer.
Supervised Learning Unsupervised Learning Restricted Boltzmann Machineread it

Feature Extraction
Feature extraction is a process by which an initial set of data is reduced by identifying key features of the data for machine learning.
Natural Language Processing Unsupervised Learning Machine Learningread it
Definitions
The data science and artificial intelligence
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