
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

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

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

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 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

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

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 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 Learningread it

Attention Models
Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Vector Neural Network Computer Visionread it
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