Definitions

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

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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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
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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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Bayes Theorem

What is Bayes' Theorem in statistics and machine learning?

Machine Learning Posterior Probability Prior Probability
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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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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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Evaluation Metrics

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

Machine Learning Confusion Matrix
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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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Manifold Hypothesis

What is the Manifold Hypothesis?

Degree of Freedom Machine Learning High-Dimensional Data
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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
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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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