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François Chollet

Deep Learning with Python, Second Edition Paperback – Dec 21 2021

by Francois Chollet (Author)

4.6 245 ratings

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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

Print length

504 pages
Language

English
Publisher

Manning
Publication date

Dec 21 2021
Dimensions

18.73 x 3.56 x 23.5 cm
ISBN-10

1617296864
ISBN-13

978-1617296864
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From the Publisher

Chollet is a master of pedagogy....... a joy to read.


	Deep Learning with Python	Grokking Deep Learning	Deep Learning with PyTorch	Deep Learning and the Game of Go	Deep Learning for Vision Systems	Probabilistic Deep Learning
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

Product description

About the Author

François Chollet is a software engineer at Google and creator of Keras.

Product details

Publisher ‏ : ‎ Manning; 2 edition (Dec 21 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 504 pages
ISBN-10 ‏ : ‎ 1617296864
ISBN-13 ‏ : ‎ 978-1617296864
Item weight ‏ : ‎ 100 g
Dimensions ‏ : ‎ 18.73 x 3.56 x 23.5 cm

Best Sellers Rank: #29,087 in Books (See Top 100 in Books)
- #15 in A.I. Neural Networks
- #26 in AI Machine Learning
- #29 in Python (Books)

Customer Reviews:
4.6 245 ratings

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François Chollet

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Customer reviews

4.6 out of 5

245 global ratings

5 star		82%
4 star		9%
3 star		3%
2 star		2%
1 star		4%

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Top reviews from Canada

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Kee

Deep Learning for a programmer

Reviewed in Canada 🇨🇦 on January 11, 2022

This book provides a good introduction for programmers that are interested in the subject.
As the author is kind to leave complex math aside while explaining the concept.
This book is a great addition to a programmer's library.

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Usman Ahmed

Book is good; seller is not

Reviewed in Canada 🇨🇦 on October 1, 2022

Verified Purchase

The contents of the book are great but the seller doesn't provide code for online access, which according to the first page of the book, should be there. Not sure if this seller is authorized to sell this book.

3 people found this helpful

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Dr. A. R. Smith

A walkthrough of his product and description of how it works without explanations

Reviewed in the United Kingdom 🇬🇧 on June 22, 2022

Verified Purchase

I'm on page 299 as I write this and and I'm a software engineer with a physics PhD who wanted a refresher on neural networks and to try some deep learning methods using tensorflow 2.0 on a side project I'm working on.

The book starts with a surface level overview of deep learning and avoided specific computer setup information (which is fine, to some extent) but it continuined in a similar manner throughout the rest of the book. He specifically says he won't include any mathematical expressions but he also doesn't give any explanation on how it works, just surface level descriptions. I'd have expected some theory (and high quality diagrams and labelling in lieu of explaination - incl axes labels in places) but it's really just an informally written walkthrough and feels like something is missing. You end up relying on a lot of background knowledge and doing a lot of leaping yourself. Perhaps a good primer for the unitiated who don't want the detail and want something working faster than I do but was disappointed with every page turn. There's probably a good book out there that does example code with underlying theory/explaination that leaves you coming away with a better understanding (rather than just knowledge/awareness) but this isn't it.

Reminds me of a bad lecture/tutorial where the guy is just trying to get through his material in too short a time period and get the mixed ability class to try the tasks and come back to him with problems.

Otherwise print is of good quality, some diagrams rushed but come out well, code formatting is reasonable. Requires basic python knowledge and familiarity with numpy. For its faults, this book does give you an overview of tensorflow by exploring some methods - no prior experience needed.

5 people found this helpful

John

Very Cheap made

Reviewed in the United Kingdom 🇬🇧 on July 31, 2022

Verified Purchase

This is a great and informative book and it really just gets to the point on how to start learning NN and deep learning,

But the actual book itself is so cheap, they clearly have cut corners on the paper for the coloured ink, you can can see straight through to the other paper to the point you can read the next page without turning over, also the actual overall package is very flimsy and I think after a few more uses this book will just disintegrate.

I wouldnt recommend buying this book if you are just going to read electronically. I however do recommend this book for its content as its brilliant. If you need to learn the mathematics side, then buy a book specific for that.

Dimitrios FOTIADIS

Excellent deep learning tutorial

Reviewed in the United Kingdom 🇬🇧 on January 24, 2023

Verified Purchase

Deep learning tutorial, excellently balanced between hands-on examples and deeper concepts explained in an intuitive, non-mathematical way. Very well structured chapters explain step by step the workflow for framing, developing and deploying a real world model. Easy to follow, uses informal language, but with great depth. Highly recommended.

YOOMIN

Book quality is excellent in condition

Reviewed in the United Kingdom 🇬🇧 on December 22, 2022

Verified Purchase

Delivered wrapped with vynil cover and good vivid colours in content

Oscar

Excelente libro

Reviewed in Mexico 🇲🇽 on May 5, 2022

Verified Purchase

Es un libro excelente, el autor explica conceptos complicados de una forma sencilla y entendible. Realmente hizo un gran trabajo de pedagogo, además estás aprendiendo del mismísimo autor de Keras, el framework más popular para machine learning.

Eso sí, es importante tener conocimiento de programación y de conceptos matemáticos (cálculo, geometría, derivación, etc) ya que el Deep Learning es básicamente eso, pura matemática; vectores, matrices, operaciones vectoriales, espacios geométricos en varias dimensiones, etc. Cabe aclarar que el libro NO usa notaciones matemáticas; para darle sencillez, el autor decide usar en su lugar líneas de código que lo hacen mucho más digerible. Sin embargo, tener el conocimiento de estos conceptos te da el poder de entender lo que se está haciendo y de lo que se está hablando.

PD: el libro en físico incluye todas las versiones digitales! Incluso Kindle!

2 people found this helpful

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François Chollet

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