Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

Quantum machine learning (QML) is the study of quantum algorithms which solve machine learning tasks. The most common use of the term refers to quantum...

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based...

non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

explainable AI (XAI), often overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables...

machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning...

In machine learning, grokking, or delayed generalization, is a phenomenon where a model abruptly transitions from overfitting (performing well only on...

outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer...

scale, and resource allocation when training a machine learning model. Comparison of deep learning software Differentiable programming All-Reduce Alex...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination...

Human Machine Learning (RHML) is an interdisciplinary approach to designing human-AI interaction systems. RHML aims to enable continual learning between...

Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...