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Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems

By: Geron, Aurelien
Material type: BookPublisher: Beijing : O'Reilly, c2019.Edition: 2nd ed.Description: xxv, 819 p. : ill. ; 24 cm.ISBN: 9781492032649Other title: Hands on machine learning with Scikit Learn and TensorFlow.Subject(s): Machine learningDDC classification: 006.31 GE HA Online resources: Location Map
Summary:
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
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Item type Home library Call number Status Notes Date due Barcode Item holds
REGULAR University of Wollongong in Dubai
Main Collection 		006.31 GE HA (Browse shelf) Available Feb2020 T0063599
Total holds: 0

Includes QR code.

Includes bibliographical references and index.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

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