Machine Learning with TensorFlow

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

Chapter 1: A machine-learning odyssey
Chapter 2: TensorFlow essentials
Chapter 3: Linear regression and beyond
Chapter 4: A gentle introduction to classification
Chapter 5: Automatically clustering data
Chapter 6: Hidden Markov models
Chapter 7: A peek into autoencoders
Chapter 8: Reinforcement learning
Chapter 9: Convolutional neural networks
Chapter 10: Recurrent neural networks
Chapter 11: Sequence-to-sequence models for chatbots
Chapter 12: Utility landscape
Appendix A: Installation