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 ∙ 2157 ∙ share

read 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) Autoencoder
07/22/2020 ∙ 2154 ∙ share

read it
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 ∙ 2035 ∙ share

read 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)
05/17/2019 ∙ 1867 ∙ share

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 Function
05/17/2019 ∙ 1717 ∙ share

read it
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 ∙ 1629 ∙ share

read 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 Probability
05/17/2019 ∙ 1615 ∙ share

read 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 Theorem
05/17/2019 ∙ 1411 ∙ share

read 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 Neurons
05/17/2019 ∙ 1406 ∙ share

read 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 Machine
05/17/2019 ∙ 1399 ∙ share

read 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 Learning
05/17/2019 ∙ 1356 ∙ share

read it