Machine Learning with TensorFlow

<b>Summary</b><br><br><i>Machine Learning with TensorFlow</i> gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. <br><br>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.<br><br><b>About the Technology</b><br><br>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.<br><br><b>About the Book</b><br><br><i>Machine Learning with TensorFlow</i> 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. <br><br><b>What's Inside</b><br><ul><li>Matching your tasks to the right machine-learning and deep-learning approaches</li><li>Visualizing algorithms with TensorBoard</li><li>Understanding and using neural networks</li></ul><br><b>About the Reader</b><br><br>Written for developers experienced with Python and algebraic concepts like vectors and matrices.<br><br><b>About the Author</b><br><br>Author <b>Nishant Shukla</b> is a computer vision researcher focused on applying machine-learning techniques in robotics. <br><br>Senior technical editor, <b>Kenneth Fricklas</b>, is a seasoned developer, author, and machine-learning practitioner.<br><br><b>Table of Contents</b><br><br><ol>PART 1 - YOUR MACHINE-LEARNING RIG<li>A machine-learning odyssey</li><li>TensorFlow essentials</li>PART 2 - CORE LEARNING ALGORITHMS<li>Linear regression and beyond</li><li>A gentle introduction to classification</li><li>Automatically clustering data</li><li>Hidden Markov models </li>PART 3 - THE NEURAL NETWORK PARADIGM<li>A peek into autoencoders</li><li>Reinforcement learning</li><li>Convolutional neural networks</li><li>Recurrent neural networks</li><li>Sequence-to-sequence models for chatbots</li><li>Utility landscape</li></ol>