Hands–On Machine Learning with Scikit–Learn and TensorFlow - Geron, Aurelien

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Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
Book Title
Hands–On Machine Learning with Scikit–Learn and TensorFlow
Genre
Machine learning
ISBN
9781491962299

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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1491962291
ISBN-13
9781491962299
eBay Product ID (ePID)
227662629

Product Key Features

Number of Pages
572 Pages
Publication Name
Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Language
English
Publication Year
2017
Subject
Intelligence (Ai) & Semantics, Data Processing, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Computers
Author
Aurélien Géron
Format
Trade Paperback

Dimensions

Item Height
1.1 in
Item Weight
34.8 Oz
Item Length
9.2 in
Item Width
7.1 in

Additional Product Features

Intended Audience
Trade
LCCN
2018-418542
Illustrated
Yes
Synopsis
Graphics in this book are printed in black and white . 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 Aur lien G ron 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. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details, Graphics in this book are printed in black and white . 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. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
LC Classification Number
Q325.5

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