Learning Algorithms

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Understanding Machine Learning

Author	: Shai Shalev-Shwartz
Publisher	: Cambridge University Press
Total Pages	: 415
Release	: 2014-05-19
Genre	: Computers
ISBN	: 1107057132

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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Understanding Machine Learning Related Books

Language: en
Pages: 415

Understanding Machine Learning

Authors: Shai Shalev-Shwartz

Categories: Computers

Type: BOOK - Published: 2014-05-19 - Publisher: Cambridge University Press

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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying thei

Language: en
Pages: 281

Learning Algorithms

Authors: George Heineman

Categories: Computers

Type: BOOK - Published: 2021-07-20 - Publisher: "O'Reilly Media, Inc."

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When it comes to writing efficient code, every software professional needs to have an effective working knowledge of algorithms. In this practical book, author

Language: en
Pages: 360

Machine Learning Algorithms

Authors: Giuseppe Bonaccorso

Categories: Computers

Type: BOOK - Published: 2017-07-24 - Publisher: Packt Publishing Ltd

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Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the

Language: en
Pages: 379

Pro Machine Learning Algorithms

Authors: V Kishore Ayyadevara

Categories: Computers

Type: BOOK - Published: 2018-06-30 - Publisher: Apress

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Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you t

Language: en
Pages: 558

Unsupervised Learning Algorithms

Authors: M. Emre Celebi

Categories: Technology & Engineering

Type: BOOK - Published: 2016-04-29 - Publisher: Springer

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This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, un