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Machine Learning with TensorFlow

Amazon.com Price:  $42.74 (as of 21/03/2019 21:03 PST- Details)

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Description

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 features 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 continuously-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 can learn the basics by working with classic prediction, classification, and clustering algorithms. Then, You can move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you are going to be able to use TensorFlow for machine-learning and deep-learning applications of your individual.

What’s Inside

  • Matching your tasks to the proper machine-learning and deep-learning approaches
  • Visualizing algorithms with TensorBoard
  • Understanding and the usage of 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 pc vision researcher focused on applying machine-learning techniques in robotics.

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

Table of Contents

    PART 1 – YOUR MACHINE-LEARNING RIG

  1. A machine-learning odyssey
  2. TensorFlow essentials

    3. PART 2 – CORE LEARNING ALGORITHMS

  3. Linear regression and beyond
  4. A gentle introduction to classification
  5. Automatically clustering data
  6. Hidden Markov models

    7. PART 3 – THE NEURAL NETWORK PARADIGM

  7. A peek into autoencoders
  8. Reinforcement learning
  9. Convolutional neural networks
  10. Recurrent neural networks
  11. Sequence-to-sequence models for chatbots
  12. Utility landscape

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