Shukla - Machine Learning With Tensorflow - 2017

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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