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Hands-On Machine Learning with Scikit-Learn and TensorFlow

Aurelien Geron

  • Bindwijze: E-book
  • Taal: en
  • Categorie: Computers & Informatica
  • ISBN: 9781491962244
Concepts, Tools, and Techniques to Build Intelligent Systems
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Taal:en
Bindwijze:E-book
Oorspronkelijke releasedatum:13 maart 2017
Ebook Formaat:Epub zonder kopieerbeveiliging (DRM)
Illustraties:Nee
Betrokkenen
Hoofdauteur:Aurelien Geron
Hoofdauteur:Aurelien Geron
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Lees dit ebook op:Desktop (Mac en Windows) , Kobo e-reader , Android (smartphone en tablet) , iOS (smartphone en tablet) , Windows (smartphone en tablet) , Overige e-reader
Overige kenmerken
Editie:1
Studieboek:Ja
Overige kenmerken
Editie:1
Studieboek:Ja

Samenvatting

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