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

By:

Géron, Aurélien

Material type: Text

TextPublication details: Mumbai: Shroff Publishers & Distributors, 2019.Edition: First editionDescription: xx, 545 pages : illustrations ; 24 cmISBN:

9781491962299
9789352135219 (Pbk)

Subject(s): Genre/Form:

Nonfiction.

DDC classification:

006.31 G319H 23

LOC classification:

Q325.5 .G47 2017

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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Holdings
Item type	Current library	Collection	Call number	Status	Notes	Date due	Barcode
Books	Central Library, IISER Bhopal Reference Section	Reference	006.31 G319H (Browse shelf(Opens below))	Not For Loan	Reserve		10155

Includes 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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