TensorFlow for Machine Intelligence: 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. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Deep Learning (Adaptive Computation and Machine Learning series)
• TensorFlow Machine Learning Cookbook: Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook
• Deep Learning with Python
• Learning TensorFlow: A Guide to Building Deep Learning Systems
• TensorFlow For Dummies
• Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
• Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
• Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
• Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras