Definitions

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

  • 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 ∙ 123

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  • 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 ∙ 120

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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.

    Neural Network Natural Language Processing Artificial Intelligence
    05/17/2019 ∙ 119

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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 ∙ 110

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  • Computational Learning Theory

    Computational Learning Theory (CoLT) is a field of AI research studying the design of machine learning algorithms to determine what sorts of problems are “learnable.”

    Probability Theory Probability Machine Learning
    05/17/2019 ∙ 103

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  • Bayesian Networks

    Bayesian networks are graphical models that use Bayesian inference to represent variables and their conditional dependencies.

    Random Variable Bayesian Inference Probability Distribution
    05/17/2019 ∙ 102

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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 ∙ 101

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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 ∙ 100

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  • Evaluation Metrics

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

    Machine Learning Confusion Matrix
    05/17/2019 ∙ 97

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  • 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.

    Machine Learning Natural Language Processing Unsupervised Learning
    05/17/2019 ∙ 95

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  • 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 ∙ 94

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