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

  • Evaluation Metrics

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

    Machine Learning Confusion Matrix
    05/17/2019 ∙ 1743

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  • 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.

    ImageNet Classifier Estimator (Statistics)
    05/17/2019 ∙ 1287

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  • 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 Posterior Probability Prior Probability
    05/17/2019 ∙ 1239

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  • 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.

    Classifier Estimator (Statistics) Autoencoder
    07/22/2020 ∙ 1205

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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 ∙ 1147

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  • Attention Models

    Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.

    Vector Neural Network Computer Vision
    05/17/2019 ∙ 1031

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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 ∙ 1012

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  • Posterior Probability

    In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.

    Probability Bayesian Inference Machine Learning
    05/17/2019 ∙ 1012

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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 Stochastic Gradient Descent
    05/17/2019 ∙ 1004

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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.

    Machine Learning Vector Neural Network
    05/17/2019 ∙ 1001

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  • Disentangled Representation Learning

    Disentangled representation is an unsupervised learning technique that breaks down, or disentangles, each feature into separate, lower dimension variables.

    Distributed Representations Unsupervised Learning Vector
    05/17/2019 ∙ 976

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