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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
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by: Aurélien Géron

 : Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Picture: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
List Price: $74.99
Amazon.com's Price: $43.99
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Binding: Paperback
EAN: 9781492032649
Edition: 2
ISBN: 1492032646
Item Dimensions: 950700280150
Label: O'Reilly Media
Languages: EnglishPublishedEnglishOriginal LanguageEnglishUnknown
Manufacturer: O'Reilly Media
MPN: 52820216
Number Of Items: 1
Number Of Pages: 856
Publication Date: October 15, 2019
Publisher: O'Reilly Media
Release Date: October 15, 2019
Studio: O'Reilly Media




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Editorial Review:

Product Description:


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


"
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