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Natural Language Processing Supervised Learning Machine Learning

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

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

Evaluation Metrics

Evaluation metrics are used to measure the quality of the statistical or machine learning model.
ImageNet Classifier Estimator (Statistics)

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.
Supervised Learning Deep Learning Loss Function

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

Attention Models

Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Machine Learning Odds (Probability) Prior Probability

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 Bayesian Inference Bayes Theorem

Posterior Probability

In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
Classifier Machine Learning Harmonic Mean


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

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

In simple words, Natural Language Processing is a field which aims to make computer systems understand human speech. NLP is comprised of techniques to process, structure, categorize raw text and extract information.