Deep Learning with Python 2nd Edition

Deep Learning with Python, Second Edition

Manning Early Access Program (MEAP)
By 作者:Chollet, Francois
Publisher Finelybook 出版社: Manning
pages 页数: 214 pages
Edition:2
Language 语言: English
ISBN-10 : 1617294438
ISBN-13 : 9781617294433

The Book Description robot was collected from Amazon and arranged by Finelybook

The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You’ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable.
about the technology
Machine learning has made remarkable progress in recent years. We’ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector
about the book
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

table of contents
1 WHAT IS DEEP LEARNING?
2 THE MATHEMATICAL BUILDING BLOCKS OF NEURAL NETWORKS
3 INTRODUCTION TO KERAS AND TENSORFLOW
4 GETTING STARTED WITH NEURAL NETWORKS:CLASSIFICATION AND REGRESSION
5 FUNDAMENTALS OF MACHINE LEARNING
6 THE UNIVERSAL WORKFLOW OF MACHINE LEARNING
7 WORKING WITH KERAS:A DEEP DIVE
8 INTRODUCTION TO DEEP LEARNING FOR COMPUTER VISION
9 ADVANCED COMPUTER VISION
10 DEEP LEARNING FOR TIMESERIES
11 DEEP LEARNING FOR TEXT
12 GENERATIVE DEEP LEARNING
13 BEST PRACTICES FOR THE REAL WORLD
14 CONCLUSIONS


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