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Deep Learning with Python Paperback – Dec 22 2017
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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 is one of the most important researchers in modern day deep learning. His groundbreaking work includes the creation of the Keras deep learning library, and major contributions to the TensorFlow framework. These tools have helped revolutionize and democratize deep learning. François is an AI researcher and Senior Staff Software Engineer at Google. François authored Deep Learning with R alongside J.J. Allaire, and developed the Abstraction and Reasoning Challenge that measures AI skill-acquisition on unknown tasks.
Table of Contents
- What is deep learning?
- Before we begin: the mathematical building blocks of neural networks
- Getting started with neural networks
- Fundamentals of machine learning
- 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
PART 1 - FUNDAMENTALS OF DEEP LEARNING
PART 2 - DEEP LEARNING IN PRACTICE
From the Publisher
For over thirty years, Manning Publications has been delivering impeccable quality in tech publishing. Our rich and independent history is filled with innovations, including groundbreaking early access programs, DRM-free ebooks, and live learning projects. We spend thousands of hours making each Manning book outstanding—and our readers agree! We’re regularly told that Manning produces the very best tech content you can buy.
Manning authors are technology experts, including distinguished academics, industry veterans, and the creators of major tools. Timeless Manning classics include Francois Chollet’s Deep Learning with Python, Jon Skeet’s C# in Depth, Don Jones’ Learn Windows Powershell in a Month of Lunches, and Chris Richarson’s Microservices Patterns. We’re proud to help some of the world’s greatest programmers share their unique insight with you.
- ISBN-109781617294433
- ISBN-13978-1617294433
- EditionFirst Edition
- PublisherManning
- Publication dateDec 22 2017
- LanguageEnglish
- Dimensions18.75 x 2.03 x 23.5 cm
- Print length384 pages
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Who should read this book
- If you’re a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning
- If you’re a deep-learning expert looking to get started with the Keras framework, you’ll find this book to be the best Keras crash course available
- If you’re a graduate student studying deep learning in a formal setting, you’ll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices
About This Book
This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you’re a practicing machine-learning engineer, a software developer, or a college student, you’ll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. You’ll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems. The code examples use the Python deep-learning framework Keras, with Tensor- Flow as a back-end engine. Keras, one of the most popular and fastest-growing deeplearning frameworks, is widely recommended as the best tool to get started with deep learning.
After reading this book, you’ll have a solid understand of what deep learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving machine-learning problems, and you’ll know how to address commonly encountered issues. You’ll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.
This book is written for people with Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers. Even technically minded people who don’t code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts.
In order to use Keras, you’ll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isn’t required. You don’t need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don’t need an advanced mathematics background, either—high school–level mathematics should suffice in order to follow along.
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Product description
About the Author
Product details
- ASIN : 1617294438
- Publisher : Manning; First Edition (Dec 22 2017)
- Language : English
- Paperback : 384 pages
- ISBN-10 : 9781617294433
- ISBN-13 : 978-1617294433
- Item weight : 100 g
- Dimensions : 18.75 x 2.03 x 23.5 cm
- Best Sellers Rank: #154,294 in Books (See Top 100 in Books)
- #5 in Speech & Audio Processing
- #23 in Online Searching
- #48 in AI Human Vision & Language Systems
- Customer Reviews:
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Customer reviews

Reviewed in Canada on June 14, 2019
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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.
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.
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).
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.
But book itself is amazing and I will 100% suggest it to anyone who wants to get into Machine Deep Learning.

Reviewed in Canada 🇨🇦 on June 14, 2019
But book itself is amazing and I will 100% suggest it to anyone who wants to get into Machine Deep Learning.

Top reviews from other countries

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.

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.

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

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.

Re the book. So far so good and it seems clearly and simply explained