Data Science & AI Glossary

Natural Language Processing Supervised Learning Machine Learning

∙ 05/17/2019

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.

Classifier Estimator (Statistics) Autoencoder

∙ 07/22/2020

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.

Machine Learning Confusion Matrix

∙ 05/17/2019

Evaluation Metrics

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

ImageNet Classifier Estimator (Statistics)

∙ 05/17/2019

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.

Machine Learning Odds (Probability) Prior Probability

∙ 05/17/2019

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.

Supervised Learning Deep Learning Loss Function

∙ 05/17/2019

Batch Normalization

Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.

Vector Neural Network Computer Vision

∙ 05/17/2019

Attention Models

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

Machine Learning Bayesian Inference Bayes Theorem

∙ 05/17/2019

Posterior Probability

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

Classifier Machine Learning Harmonic Mean

∙ 05/17/2019

F-Score

The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.

Supervised Learning Unsupervised Learning Restricted Boltzmann Machine

∙ 05/17/2019

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.

Natural Language Processing Unsupervised Learning Machine Learning

∙ 05/17/2019

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.

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