Normal view MARC view ISBD view

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

By: Geron, Aurelien.
Material type: TextTextPublisher: New Delhi : Shroff Publishers and Distributors pvt. Ltd., 2017Description: xx, 551 pages : illustrations ; 24 cm.ISBN: 1491962291; 9781491962299; 9789352135219.Subject(s): Machine learning | Artificial intelligence | COMPUTERS / Computer Vision & Pattern Recognition | COMPUTERS / Data Processing | COMPUTERS / Intelligence (AI) & Semantics | Artificial intelligenceDDC classification: 006.31 Online resources: WorldCat Details
Contents:
Table of contents 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" --
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
Item type Current location Collection Call number Copy number Status Date due Barcode Item holds
Text Text EWU Library
Reserve Section
Non-fiction 006.31 GEH 2018 (Browse shelf) C-1 Not For Loan 29941
Text Text EWU Library
Circulation Section
Non-fiction 006.31 GEH 2018 (Browse shelf) C-2 Checked out 28/08/2019 29942
Total holds: 0

Includes index.

Table of contents 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" --

Computer Science & Engineering Computer Science & Engineering

There are no comments for this item.

Log in to your account to post a comment.

Library Home | Contacts | E-journals
Copyright @ 2011-2019 EWU Library
East West University