Natural Language Processing Supervised Learning Machine Learning∙ 05/17/2019
Active LearningActive learning is a form of semi-supervised machine learning where the algorithm chooses which data to learn from and queries a teacher for guidance.
Classifier Estimator (Statistics) Autoencoder∙ 07/22/2020
Generative Adversarial NetworkA generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.
Machine Learning Confusion Matrix∙ 05/17/2019
Evaluation MetricsEvaluation metrics are used to measure the quality of the statistical or machine learning model.
ImageNet Classifier Estimator (Statistics)∙ 05/17/2019
Convolutional Neural NetworkA convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.
Supervised Learning Deep Learning Loss Function∙ 05/17/2019
Batch NormalizationBatch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.
Vector Neural Network Computer Vision∙ 05/17/2019
Attention ModelsAttention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Machine Learning Odds (Probability) Prior Probability∙ 05/17/2019
Bayes TheoremBayes’ 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 Bayesian Inference Bayes Theorem∙ 05/17/2019
Posterior ProbabilityIn statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
Classifier Machine Learning Harmonic Mean∙ 05/17/2019
F-ScoreThe F score, also called the F1 score or F measure, is a measure of a test’s accuracy.
Supervised Learning Unsupervised Learning Restricted Boltzmann Machine∙ 05/17/2019