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Viser: Machine Learning with TensorFlow

Machine Learning with TensorFlow

Machine Learning with TensorFlow

Nishant Shukla
(2018)
Sprog: Engelsk
Manning Publications Company
298,00 kr.
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Detaljer Om Varen

  • Paperback: 272 sider
  • Udgiver: Manning Publications Company (Februar 2018)
  • ISBN: 9781617293870

Summary

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.

About the Book

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

What's Inside

  • Matching your tasks to the right machine-learning and deep-learning approaches
  • Visualizing algorithms with TensorBoard
  • Understanding and using neural networks

About the Reader

Written for developers experienced with Python and algebraic concepts like vectors and matrices.

About the Author

Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics.

Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.

Table of Contents

PART 1 - YOUR MACHINE-LEARNING RIG
  • A machine-learning odyssey
  • TensorFlow essentials
  • PART 2 - CORE LEARNING ALGORITHMS
  • Linear regression and beyond
  • A gentle introduction to classification
  • Automatically clustering data
  • Hidden Markov models
  • PART 3 - THE NEURAL NETWORK PARADIGM
  • A peek into autoencoders
  • Reinforcement learning
  • Convolutional neural networks
  • Recurrent neural networks
  • Sequence-to-sequence models for chatbots
  • Utility landscape
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