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 ∙ 2157

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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 ∙ 2154

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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 ∙ 2035

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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 ∙ 1867

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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 ∙ 1717

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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 ∙ 1629

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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 Odds (Probability) Prior Probability
    05/17/2019 ∙ 1615

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  • Posterior Probability

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

    Machine Learning Bayesian Inference Bayes Theorem
    05/17/2019 ∙ 1411

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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 Neurons
    05/17/2019 ∙ 1406

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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 ∙ 1399

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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.

    Natural Language Processing Unsupervised Learning Machine Learning
    05/17/2019 ∙ 1356

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