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Deep Learning with Python by [Francois Chollet]

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Francois Chollet

Deep Learning with Python 1st Edition, Kindle Edition

by Francois Chollet (Author) Format: Kindle Edition

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Summary

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. 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.

What's Inside

Deep learning from first principles
Setting up your own deep-learning environment
Image-classification models
Deep learning for text and sequences
Neural style transfer, text generation, and image generation

About the Reader

Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the Author

François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING
What is deep learning?
Before we begin: the mathematical building blocks of neural networks
Getting started with neural networks
Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
Deep learning for computer vision
Deep learning for text and sequences
Advanced deep-learning best practices
Generative deep learning
Conclusions
appendix A - Installing Keras and its dependencies on Ubuntu
appendix B - Running Jupyter notebooks on an EC2 GPU instance

ISBN-13

978-1617294433
Edition

1st
Sticky notes

On Kindle Scribe
Publisher

Manning
Publication date

Nov. 30 2017
Language

English
File size

10011 KB
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Product description

About the Author

François Chollet is a software engineer at Google and creator of the Keras deep-learning library. --This text refers to an alternate kindle_edition edition.

Product details

ASIN ‏ : ‎ B0977ZRV1J
Publisher ‏ : ‎ Manning; 1st edition (Nov. 30 2017)
Language ‏ : ‎ English
File size ‏ : ‎ 10011 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Sticky notes ‏ : ‎ On Kindle Scribe
Print length ‏ : ‎ 385 pages

Best Sellers Rank: #515,071 in Kindle Store (See Top 100 in Kindle Store)
- #41 in Speech & Audio Processing
- #118 in Online Searching
- #200 in AI Human Vision & Language Systems

Customer Reviews:
1,349 ratings

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Customer reviews

4.6 out of 5

1,349 global ratings

5 star		74%
4 star		15%
3 star		6%
2 star		2%
1 star		3%

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FM Saouda

Concise, Great Examples, Gets to the Point

Reviewed in Canada 🇨🇦 on October 14, 2022

Verified Purchase

Book was worth every penny. Well written. Great examples.

Pair this up with Geron (Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition), and you'll get some great theoretical and practical exercises in ML.

Helpful

Bilal

Should've been titled: Deep learning with the Keras framework and TensorFlow

Reviewed in Canada 🇨🇦 on June 21, 2019

Verified Purchase

Excellent book to get a quick start on deep learning! This is not a book to learn the theoretical aspects of deep-learning, rather it is a collection of hands-on examples to work through and learn by experience and the guidance provided by the author. That said, if you have seen neural networks from the 1990s along with the back propagation algorithm, and you can visualize the concepts of gradient descent and convolution, then this material is very easy to follow

The examples are setup on the Keras framework using TensorFlow as the backend engine. I used an EC2 p2.xlarge instance as suggested by the author. The setup required a bit of help beyond what's provided in Appendix B. Once setup though you will need to run from a virtual environment: "source activate tensorflow_p36". . . . . . My final thought is that after having read Chapter 7, I want to do a second pass using callbacks and tensorboard for better insight.

One person found this helpful

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Matt Allen

One of the best summary books

Reviewed in Canada 🇨🇦 on August 11, 2021

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I enjoyed reading it. Written by the guy who developed Keras. Very easy to follow.

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Lino Coria Mendoza

Great book to implement deep learning algorithms with Python

Reviewed in Canada 🇨🇦 on February 16, 2018

Verified Purchase

This is a great book. In order to follow this material, it is useful to know the basics of machine learning. You should also be very familiar with Python. I really like that equations have been replaced with lines of code. Following lines of code is easier for many people, including myself (there are other books for purists). The Jupyter notebooks are a great way to learn the basics. I also like all the hands-on advice (special attention is given to the problem of over-fitting).
Potential problem: a lot of the information might become outdated very soon. Make sure you get the latest edition of the book (as more editions become available).

2 people found this helpful

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William Mailhot

Really good quality

Reviewed in Canada 🇨🇦 on February 3, 2019

Verified Purchase

The quality of the content is amazing and very well explained. The only reason I gave the book a 4/5 is because of the physical book itself. The book is very fragile and the pages are very thin. The book I received was also damaged (has folds on it that are irreversible)

One person found this helpful

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James

Buyer beware, I got a counterfeit book

Reviewed in Canada 🇨🇦 on March 30, 2020

Verified Purchase

I only purchased this book, because I wanted a reference book for Deep learning but could not wait for 2nd edition. Seeing the discounted price, I thougth I would go ahead with the purchase, and just buy 2nd edition again anyway later this year.

This review is not about the content of the book, but of the book's quality. Specifically I have a suspicion that the book I received was counterfeit. I say this for the following reasons:
1) there is an insert that allows me to download the e-book for free from the publisher's website. However, the insert is littered with typos, of the automatic OCR scan-to-text type. Eg link is tink, etc.
2) the binding looks a bit glue-gunned, especially to this insert
3) the graphics look pixelated (especially the graphs), the print looks like it was done in economode, and the paper is recycled/grey looking.

Therefore, be mindful of the purchase and gauge from the selling price. Remember that the MSRP is approx $65.

7 people found this helpful

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Rem Kim

Book layout is awful, but book content is amazing

Reviewed in Canada 🇨🇦 on June 14, 2019

Verified Purchase

Don't get me wrong the book itself is AMAZING, its just the way its printed is completely awful. Every left page is so close to the middle I have to bend left pages very hard just so i can read it comfortably. EVERY LEFT PAGE.

But book itself is amazing and I will 100% suggest it to anyone who wants to get into Machine Deep Learning.

3 people found this helpful

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A. Gontcharov

The BEST

Reviewed in Canada 🇨🇦 on November 23, 2018

Verified Purchase

This is hands down, the BEST book on Deep Learning for beginners. The author is super knowledgeable, covers all the state-of-the-art techniques, and is an absolutely amazing teacher. I couldn't put the book down. I followed all the step-by-step instructions, and finally took advantage of my GPU to train NNs. Highly recommended. I have tried many books until I stumbled upon this one. Plus, he is one of the creators of Keras!

One person found this helpful

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Top reviews from other countries

SwedishMike

The best introduction to this very interesting field that I have found

Reviewed in the United Kingdom 🇬🇧 on February 7, 2018

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This book is making something as intricate and advanced as deep learning understandable in a very clear and concise way.

If you want to get started with Keras, deep learning, neural networks and all that - this is one of the best books I've ever seen. If not the best.

It doesn't go full tilt into all the mathematics behind it - something I appreciate - but it sure gives you enough to get you started as well as a good way towards the more advanced subjects in this field. If you want all the formulas and algorithms behind this - there are better books but if you want to hit the ground running this is the book for you.

I don't think I can recommend this book highly enough.

12 people found this helpful

Mr. Francis A. King

An excellent book. You will learn much.

Reviewed in the United Kingdom 🇬🇧 on June 27, 2019

Verified Purchase

This book is written by someone who clearly has two major abilities: they have a love of the subject, and they communicate it clearly.

The book contains real examples of Python/Keras code to do deep learning on standard data sets. Some knowledge of Python is required, but I think that any competent programmer can get this as they go along. I certainly improved my Python while working through the examples.

The author makes clear their belief that a Linux system is required to do the examples in the book. This is the author's only major mistake. I have tried the examples under Windows 10/Anaconda 3 and they simply work. Perhaps the GPU based examples work better under Linux - I didn't try these.

After finishing the book, the reader will be well placed to know the basics of deep learning, and to take the subject further.

5 people found this helpful

nikki

Content is clear - following code will drive you mad!

Reviewed in the United Kingdom 🇬🇧 on May 21, 2021

Verified Purchase

The content is clear and ideas are succinct. I am a masters AI student so to see the material written this way is great and fairly straight forward. If you are not familiar with DL at all read blogs/articles first. If you are then it will be easy to follow.

However, if you want to follow along with some very simple exercises don't expect to get the same results (i.e. loss or accuracy as francis attains with the provided code). In other words he's half assed this, if he's reading this get it sorted asap mate! (very poor and frustrating for the standard at which he is working at). All of the code should be replicable within ~1% here and there as far as i'm concerned.

For practical hands on experience get onto a udemy course for £15.
For a quick and concise guide its okay as well - not read other books as of yet which are as accessible without heavy math and programming so from this point its well done

3 people found this helpful

hfffoman

Practical and easy to learn from

Reviewed in the United Kingdom 🇬🇧 on November 13, 2018

Verified Purchase

This is a hands on practical book for people who want to get into deep learning quickly. It requires knowledge of python but almost no knowledge of AI, explaining for instance the basic concepts of annotation, labelled instances and the difference between supervised and unsupervised learning.

It starts with a series on simple practical examples which the reader can easily reproduce and explore alone. The explanations are readable and understandable away from a computer (I read much of it on holiday). It then goes into detail of the two most advanced applications of deep learning - image processing and text processing.

The notation throughout is python rather than formal mathematical notation. If you like reading code but don't like reading matrix equations, this will be ideal. The one possible shortcoming is that it veers heavily to the practical side and isn't concerned with the theory. Thus it doesn't explain how backprop works or even give you the equations, merely noting that most packages automate them so you might as well not waste your time and get on with learning how to do it. This is perhaps a wise approach since Hinton's excellent coursera lectures are freely available and are both accessible and rigorous.

One person found this helpful

AndyB

Wondering where the Kindle version is? Wonder no more!

Reviewed in the United Kingdom 🇬🇧 on May 14, 2018

Verified Purchase

Not sure it states it in the product info but when you buy the book you can register it and get a Kindle / PDF / EPub version for free.
Re the book. So far so good and it seems clearly and simply explained

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Follow the Author

Deep Learning with Python 1st Edition, Kindle Edition

PART 1 - FUNDAMENTALS OF DEEP LEARNING

PART 2 - DEEP LEARNING IN PRACTICE

Product description

About the Author

Product details

About the author

Francois Chollet

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