# Definitions

The data science and artificial intelligence
terms you need while reading the latest research

• ### Evaluation Metrics

Evaluation metrics are used to measure the quality of the statistical or machine learning model.

Machine Learning Confusion Matrix
05/17/2019 ∙ 2163

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) Autoencoder
07/22/2020 ∙ 2127

• ### Active Learning

Active learning is a form of semi-supervised machine learning where the algorithm chooses which data to learn from and queries a teacher for guidance.

Natural Language Processing Supervised Learning Machine Learning
05/17/2019 ∙ 1817

• ### 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)
05/17/2019 ∙ 1811

• ### 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 Function
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• ### Attention Models

Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.

Vector Neural Network Computer Vision
05/17/2019 ∙ 1628

• ### 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 Probability
05/17/2019 ∙ 1599

• ### 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 Neurons
05/17/2019 ∙ 1431

• ### Posterior Probability

In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.

Machine Learning Bayesian Inference Bayes Theorem
05/17/2019 ∙ 1404

• ### 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 Machine
05/17/2019 ∙ 1388