UPC: 9781617293870 | Machine Learning with TensorFlow (Edition 1) (Paperback)

UPC lookup results for: 9781617293870 | Machine Learning with TensorFlow (Edition 1) (Paperback)

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 PART 1 – YOUR MACHINE-LEARNING RIG A machine-learning odyssey TensorFlow essentials PART 2 – CORE LEARNING ALGORITHMS Linear regression and beyond A gentle introduction to classification Automatically clustering data Hidden Markov models PART 3 – THE NEURAL NETWORK PARADIGM A peek into autoencoders Reinforcement learning Convolutional neural networks Recurrent neural networks Sequence-to-sequence models for chatbots Utility landscape