
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

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

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

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

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

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 reductionread 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

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 Distributionread it

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