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

Convolutional Neural Network
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.
ImageNet Classifier Estimator (Statistics)read 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

Generative Adversarial Network
A generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.
Classifier Estimator (Statistics) Autoencoderread 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 Odds (Probability) Prior Probabilityread 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

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

Posterior Probability
In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
Machine Learning Bayesian Inference Bayes Theoremread 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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Deep Belief Network
Deep Belief Networks (DBNs) are a laddering of individual unsupervised networks that use each network’s hidden layer as the input for the next layer.
Supervised Learning Unsupervised Learning Restricted Boltzmann Machineread 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
Definitions
The data science and artificial intelligence
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