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

By: Geron, AurelienMaterial type: TextTextLanguage: English Publication details: New Delhi : Shroff Publishers and Distributors pvt. Ltd., 2017. Description: xx, 551 pages : illustrations ; 24 cmISBN: 1491962291; 9781491962299; 9789352135219Subject(s): Machine learning | Artificial intelligence | COMPUTERS / Computer Vision & Pattern Recognition | COMPUTERS / Data Processing | COMPUTERS / Intelligence (AI) & Semantics | Artificial intelligenceDDC classification: 006.31 LOC classification: Q325.5 | .G47 2017Online resources: WorldCat Details
Contents:
TOC Preface Part I -- The fundamentals of machine learning -- The machine learning landscape -- End-to-end machine learning project -- Classification -- Training models -- Support vector machines -- Decision trees -- Ensemble learning and random forests -- Dimensionality reduction Part II -- Neural networks and deep learning -- Up and running with TensorFlow -- Introduction to artificial neural networks -- Training deep neural nets -- Distributing TensorFlow across devices and servers -- Convolutional neural networks -- Recurrent neural networks -- Autoencoders -- Reinforcement learning -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures -- Index
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 Current library Collection Call number Copy number Status Date due Barcode Item holds
Text Text EWU Library
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Non-fiction 006.31 GEH 2018 (Browse shelf(Opens below)) C-1 Not For Loan 29941
Text Text EWU Library
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Non-fiction 006.31 GEH 2018 (Browse shelf(Opens below)) C-2 Checked out 19/05/2022 29942
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Includes index.

TOC Preface Part I --
The fundamentals of machine learning --
The machine learning landscape --
End-to-end machine learning project --
Classification --
Training models --
Support vector machines --
Decision trees --
Ensemble learning and random forests --
Dimensionality reduction Part II --
Neural networks and deep learning --
Up and running with TensorFlow --
Introduction to artificial neural networks --
Training deep neural nets --
Distributing TensorFlow across devices and servers --
Convolutional neural networks --
Recurrent neural networks --
Autoencoders --
Reinforcement learning --
Exercise solutions --
Machine learning project checklist --
SVM dual problem --
Autodiff --
Other popular ANN architectures --
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" --

CSE CSE

Sagar Shahanawaz

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