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** PART 1 - YOUR MACHINE-LEARNING RIG2. A machine-learning odyssey 3. TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS5. Linear regression and beyond 6. A gentle introduction to classification 7. Automatically clustering data 8. Hidden Markov models PART 3 - THE NEURAL NETWORK PARADIGM10. A peek into autoencoders 11. Reinforcement learning 12. Convolutional neural networks 13. Recurrent neural networks 14. Sequence-to-sequence models for chatbots 15. Utility landscape Annotation Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. This book teaches readers about machine learning algorithms and how to implement solutions with TensorFlow