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

1. A machine-learning odyssey
2. TensorFlow essentialsPART 2 - CORE LEARNING ALGORITHMS
3. Linear regression and beyond
4. A gentle introduction to classification
5. Automatically clustering data
6. Hidden Markov models 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

Autorentext

AUTHOR BIO

Nishant Shukla is a computer vision researcher at UCLA, focusing on

machine learning techniques with robotics. He has been a developer for

Microsoft, Facebook, and Foursquare, and a machine learning engineer for

SpaceX, as well as the author of the Haskell Data Analysis Cookbook.

Klappentext

KEY FEATURES

• Lots of diagrams, code examples, and exercises

• Solves real-world problems with TensorFlow

• Uses well-studied neural network architectures

• Presents code that can be used for the readers' own applications

AUDIENCE

This book is for programmers who have some experience with Python and

linear algebra concepts like vectors and matrices. No experience with

machine learning is necessary.

Zusammenfassung
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

1. A machine-learning odyssey
2. TensorFlow essentials

PART 2 - CORE LEARNING ALGORITHMS

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

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