Machine Learning with TensorFlow

Machine Learning with TensorFlow

Ratings : 4/5 from 1181 reviews
Publisher : Manning Publications
ISBN : 1617293873
Release Date :

Examined by :

Apr, 22 2018

Product 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 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

  1. A machine-learning odyssey
  2. TensorFlow essentials
  3. PART 2 - CORE LEARNING ALGORITHMS

  4. Linear regression and beyond
  5. A gentle introduction to classification
  6. Automatically clustering data
  7. Hidden Markov models
  8. PART 3 - THE NEURAL NETWORK PARADIGM

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