
-
Evaluation Metrics
Evaluation metrics are used to measure the quality of the statistical or machine learning model.
Machine Learning Confusion Matrixread 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
-
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 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
-
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
-
Attention Models
Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Vector Neural Network Computer Visionread 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
-
Disentangled Representation Learning
Disentangled representation is an unsupervised learning technique that breaks down, or disentangles, each feature into separate, lower dimension variables.
Distributed Representations Unsupervised Learning Neuronsread it
-
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
-
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.
read it