Machine Learning with TensorFlow
Nishant Shukla, "Machine Learning with TensorFlow"
ISBN: 1617293873 | 2018 | EPUB | 272 pages | 7 MB
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