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.

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

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

1. PART 1 - YOUR MACHINE-LEARNING RIG
2. A machine-learning odyssey
3. TensorFlow essentials
4. PART 2 - CORE LEARNING ALGORITHMS
5. Linear regression and beyond
6. A gentle introduction to classification
7. Automatically clustering data
8. Hidden Markov models
9. PART 3 - THE NEURAL NETWORK PARADIGM
10. A peek into autoencoders
11. Reinforcement learning
12. Convolutional neural networks
13. Recurrent neural networks
14. Sequence-to-sequence models for chatbots
15. Utility landscape