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.
A generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.
Evaluation metrics are used to measure the quality of the statistical or machine learning model.
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.
Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.
Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
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.
In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.
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.
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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