A hands-on introduction to learning algorithms
TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google.
TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters.
TABLE OF CONTENTS
Part I. Getting Started with TensorFlow
CHAPTER 1: Introduction
CHAPTER 2: TensorFlow Installation
Part II. TensorFlow and Machine Learning Fundamentals
CHAPTER 3: TensorFlow Fundamentals
CHAPTER 4: Machine Learning Basics
Part III. Implementing Advanced Deep Models in TensorFlow
CHAPTER 5: Object Recognition and Classification
CHAPTER 6: Recurrent Neural Networks and Natural Language Processing
Part IV. Additional Tips, Techniques, and Features
CHAPTER 7: Deploying models in production
CHAPTER 8: Helper Functions, Code Structure, and Classes
CHAPTER 9: Conclusion
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