Definitions

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

Deep Learning

Deep learning is a machine learning method using multiple layers of nonlinear processing units to extract features from data. Find out more on DeepAI.

Natural Language Processing Machine Learning Computer Vision
05/17/2019 ∙ 106 ∙ share

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Adversarial Machine Learning

Adversarial Machine Learning is a collection of techniques to train neural networks on how to spot intentionally misleading data or behaviors.

Vector Machine Learning Defensive Distillation
05/17/2019 ∙ 105 ∙ share

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Convolutional Neural Networks

A convolutional neural network is a type of neural network that is most commonly applied to processing and analyzing visual imagery.

Natural Language Processing Machine Learning Deep Learning
05/17/2019 ∙ 93 ∙ share

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Neural Network

What is a neural network, and how is it related to machine learning and artificial intelligence?

Machine Learning Classifier Neurons
05/17/2019 ∙ 87 ∙ share

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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 ∙ 80 ∙ share

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Early Stopping

What is early Stopping in machine learning?

Epoch Machine Learning Variance
05/17/2019 ∙ 74 ∙ share

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Gaussian Mixture Model

A Gaussian mixture model is a probabilistic model for representing normally distributed subpopulations among a larger population.

Unsupervised Learning Variance Estimator (Statistics)
05/17/2019 ∙ 73 ∙ share

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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 ∙ 72 ∙ share

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Particle Swarm Optimization

Particle swarm optimization (PSO) is an algorithm that uses swarm intelligence to solve problems that can be represented as a point or surface in a multi-dimensional space.

05/17/2019 ∙ 70 ∙ share

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Autoencoder

An autoencoder is an unsupervised learning technique for neural networks that learns efficient data representations (encoding) by training the network to ignore signal “noise.”

Unsupervised Learning Denoising Autoencoders Contractive Autoencoder
05/17/2019 ∙ 69 ∙ share

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Feature Selection

Feature selection is the process by which a subset of features, or variables, are selected from a large dataset for building machine learning models.

Machine Learning Natural Language Processing Feature reduction
05/17/2019 ∙ 68 ∙ share

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