Overview
- Focus on taking deep learning models to production
- Practical and hands-on approach
- Covers popular Python frameworks
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Table of contents (14 chapters)
Keywords
About this book
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python alsointroduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.
What You Will Learn
- Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
- Gain the fundamentals of deep learning with mathematical prerequisites
- Discover the practical considerations of large scale experiments
- Take deep learning models to production
Who This Book Is For
Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Deep Learning with Python
Book Subtitle: A Hands-on Introduction
Authors: Nikhil Ketkar
DOI: https://doi.org/10.1007/978-1-4842-2766-4
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Nikhil Ketkar 2017
Softcover ISBN: 978-1-4842-2765-7Published: 19 April 2017
eBook ISBN: 978-1-4842-2766-4Published: 18 April 2017
Edition Number: 1
Number of Pages: XVII, 226
Number of Illustrations: 28 b/w illustrations, 65 illustrations in colour
Topics: Artificial Intelligence, Programming Techniques, Programming Languages, Compilers, Interpreters, Mathematical Logic and Formal Languages