Definitions

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

  •

    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 ∙ 179 ∙

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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 ∙ 173 ∙

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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 ∙ 140 ∙

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  • 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.

    05/17/2019 ∙ 139 ∙

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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 ∙ 134 ∙

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  • Tensor

    A Tensor is a mathematical object similar to, but more general than, a vector and often represented by an array of components that describe functions relevant to coordinates of a space. Put simply, a Tensor is an array of numbers that transform according to certain rules under a change of coordinates.

    Neural Network Vector Machine Learning
    05/17/2019 ∙ 133 ∙

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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 ∙ 123 ∙

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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 ∙ 122 ∙

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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 ∙ 118 ∙

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  • Statistical Learning Theory

    Statistical learning theory is the broad framework for studying the concept of inference in both supervised and unsupervised machine learning.

    Probability Machine Learning Deep Learning
    05/17/2019 ∙ 118 ∙

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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 ∙ 116 ∙

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