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Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101491962291
ISBN-139781491962299
eBay Product ID (ePID)227662629
Product Key Features
Number of Pages572 Pages
Publication NameHands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
LanguageEnglish
Publication Year2017
SubjectIntelligence (Ai) & Semantics, Data Processing, Computer Vision & Pattern Recognition
TypeTextbook
Subject AreaComputers
AuthorAurélien Géron
FormatTrade Paperback
Dimensions
Item Height1.1 in
Item Weight34.8 Oz
Item Length9.2 in
Item Width7.1 in
Additional Product Features
Intended AudienceTrade
LCCN2018-418542
IllustratedYes
SynopsisGraphics 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 NumberQ325.5
Bewertungen und Rezensionen
Relevanteste Rezensionen
- 03. Feb. 2020
Book purchased
It said there are some scratches on the cover of the book, but it’s very new and I cannot even recognize it’s old. Thanks.Bestätigter Kauf: JaArtikelzustand: Gebraucht
- 17. Feb. 2021
Excellent book for one who machine learning beginners
AwesomeBestätigter Kauf: JaArtikelzustand: Gebraucht
- 24. Mär. 2020
Good
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- 21. Feb. 2018
Five Stars
Thank you!!!Bestätigter Kauf: JaArtikelzustand: Gebraucht
