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 ∙ 1146

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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 ∙ 1016

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  • Generative Adversarial Network

    A generative adversarial network (GAN) is an unsupervised machine learning technique that trains two neural networks by forcing them to “outwit” each other.

    Classifier Unsupervised Learning Machine Learning
    05/17/2019 ∙ 996

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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 ∙ 991

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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 ∙ 888

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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 ∙ 887

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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 ∙ 869

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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 ∙ 843

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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 ∙ 842

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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 ∙ 820

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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 ∙ 798

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