
Active Learning
Active learning is a form of semisupervised machine learning where the algorithm chooses which data to learn from and queries a teacher for guidance.
Natural Language Processing Supervised Learning Machine Learningread it

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 Intelligenceread it

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.
read it

Bayes Theorem
What is Bayes' Theorem in statistics and machine learning?
Machine Learning Posterior Probability Prior Probabilityread it

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 Learningread it

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 Functionread it

Evaluation Metrics
Evaluation metrics are used to measure the quality of the statistical or machine learning model.
Machine Learning Confusion Matrixread it

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 Learningread it

Manifold Hypothesis
What is the Manifold Hypothesis?
Degree of Freedom Machine Learning HighDimensional Dataread it

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 Learningread it

Bayesian Networks
Bayesian networks are graphical models that use Bayesian inference to represent variables and their conditional dependencies.
Random Variable Bayesian Inference Probability Distributionread it
Definitions
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