Price: $59.99 - $41.61
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Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.
In Deep Learning with Python, Second Edition you will learn:
Deep learning from first principles
Image classification and image segmentation
Timeseries forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout
Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started.
About the book
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications.
What’s inside
Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout
About the reader
For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the author
François Chollet is a software engineer at Google and creator of the Keras deep-learning library.
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 deep learning for 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
From the Publisher
Customer Reviews
4.7 out of 5 stars
382
4.4 out of 5 stars
164
4.4 out of 5 stars
121
4.4 out of 5 stars
55
4.7 out of 5 stars
73
4.3 out of 5 stars
19
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User Experience Level
Intermediate Beginner Intermediate Intermediate Intermediate Experienced
Readers Who Want
Deep learning from the ground up. Friendly illustrated tutorial on deep learning fundamentals Professional guide to image and text processing with PyTorch Apply deep learning by building a complete project Serious introduction to deep learning-based image processing Bayesian inference and probablistic programming for deep learning
Compatible with
Python 3 Python 3 Python 3 Python 3 Python 3 Python 3
Special Features
Written by Keras creator François Chollet Learn core deep learning algorithms using only high school mathematics. Includes an in-depth look at identifying anomalies in medical images Build a complete Go-playing bot that can challenge serious players A comprehensive look at deep learning for image recommendation and classification Create deep learning systems that return ranges of results based on probability
Praise
“Chollet explains complex concepts with minimal fuss. A joy to read.” — Martin Görner, Google “From a masterful teacher who guides, illuminates, and encourages you along the way.” — Kelvin D. Meeks, International Technology Ventures “We finally have a definitive treatise on PyTorch.” — From the Foreword by Soumith Chintala, Co-creator of PyTorch “Inspired and inspiring. Highly recommended!” — Burk Hufnagel, Daugherty Business Solutions “Real-world problem solving without drowning you in details.” — Burhan Ul Haq, Audit XPRT “Comprehensive walkthrough with lots of practical examples.” — Diego Casella, Centrica Business Solutions, Belgium
Page Count
504 336 520 384 480 296
Publisher : Manning; 2nd edition (December 21, 2021)
Language : English
Paperback : 504 pages
ISBN-10 : 1617296864
ISBN-13 : 978-1617296864
Item Weight : 2.35 pounds
Dimensions : 7.38 x 1.3 x 9.25 inches
Customers say
Customers find the book very educational and helpful. They say it provides a much broader understanding of deep learning with intuitive explanations and plentiful examples. Readers also mention the writing is clear, deftly navigated, and easy to follow. They appreciate the colorful pictures and codes that are well-explained.
AI-generated from the text of customer reviews
