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

4.5
ISBN-13: 9781492032649
ISBN-10: 1492032646
Edition: 2
Author: Aurélien Géron
Publication date: 2019
Publisher: O'Reilly Media
Format: Paperback 856 pages
Category: Computers
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Book details

ISBN-13: 9781492032649
ISBN-10: 1492032646
Edition: 2
Author: Aurélien Géron
Publication date: 2019
Publisher: O'Reilly Media
Format: Paperback 856 pages
Category: Computers

Summary

Acknowledged authors Aurélien Géron wrote Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems comprising 856 pages back in 2019. Textbook and eTextbook are published under ISBN 1492032646 and 9781492032649. Since then Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems textbook received total rating of 4.5 stars and was available to sell back to BooksRun online for the top buyback price of $ 21.85 or rent at the marketplace.

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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