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 ∙ 281

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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 ∙ 219

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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.

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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
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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
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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
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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
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  • Feature Selection

    Feature selection is the process by which a subset of features, or variables, are selected from a large dataset for building machine learning models.

    Machine Learning Natural Language Processing Feature reduction
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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
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  • Principle of Maximum Entropy

    The principle of maximum entropy requires selecting the most unpredictable (maximum entropy) prior probability if only a single parameter is known about a probability distribution.

    Prior Probability Geometric Distribution Exponential Distribution
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  • Hidden Markov Model

    Hidden Markov Model is a statistical Markov model in which the model states are hidden.

    Probability Markov Model Probability Distribution
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