Learn top data science and A.I. terms.

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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

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.