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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.
Microsoft Azure, or just Azure (/ˈæʒər, ˈeɪʒər/ AZH-ər, AY-zhər, UK also /ˈæzjʊər, ˈeɪzjʊər/ AZ-ure, AY-zure), [5] [6] [7] is the cloud computing platform developed by Microsoft. It has management, access and development of applications and services to individuals, companies, and governments through its global infrastructure.
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Microsoft Learn is a library of technical documentation and training for end users, developers, and IT professionals who work with Microsoft products. Microsoft Learn was introduced in September 2018. [1] In 2022, Microsoft Docs, the technical documentation library that had replaced MSDN and TechNet in 2016, was moved to Microsoft Learn. [2] [3]
The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns , Yishay Mansour , Dana Ron , Ronitt Rubinfeld , Robert Schapire and Linda Sellie in 1994 [ 1 ] and it was inspired from the PAC-framework introduced by Leslie Valiant .
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
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