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Deep Learning with Python First Edition
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Purchase options and add-ons
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, Tensor Flow, 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 Tensor Flow 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 Jupiter notebooks on an EC2 GPU instance.
- ISBN-109781617294433
- ISBN-13978-1617294433
- EditionFirst Edition
- PublisherManning
- Publication dateDecember 22, 2017
- LanguageEnglish
- Dimensions7.38 x 0.8 x 9.25 inches
- Print length384 pages
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From the Publisher
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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Price | $47.49$47.49 | $29.99$29.99 |
Deep Learning with Francois Chollet |
Editorial Reviews
Review
'An accessible quick revision guide with all the essential information in one place which makes a good addition to textbooks and other study material.' J oanne Atkinson, Director of Postgraduate Law Programmes, University of Portsmouth
'... excellent companion for students. It is to be used as a revision guide and will be useful for students who are conversant with the principles and case law of each topic.' Alison Poole, Teaching Fellow, University of Portsmouth
'This series is great - after having revised everything, it showed me a way to condense all the information and gave me an idea of how I would go about structuring my essays.' Arama Lemon, Student, Coventry University
'The Law Express Q&A series is perfect as it targets different learning styles - it includes diagrams and flowcharts that you can follow for easy application with confidence. It's perfect for anyone who wants to receive an extra boost with their revision!' Mariam Hussain, Student, University of Westminster
From the Back Cover
Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects.
Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.
About the Author
Product details
- ASIN : 1617294438
- Publisher : Manning; First Edition (December 22, 2017)
- Language : English
- Paperback : 384 pages
- ISBN-10 : 9781617294433
- ISBN-13 : 978-1617294433
- Item Weight : 1.42 pounds
- Dimensions : 7.38 x 0.8 x 9.25 inches
- Best Sellers Rank: #571,642 in Books (See Top 100 in Books)
- #281 in Computer Neural Networks
- #592 in Python Programming
- #1,231 in Artificial Intelligence & Semantics
- Customer Reviews:
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Learn more how customers reviews work on AmazonCustomers say
Customers find the book provides good coverage of deep learning techniques and concepts. It explains topics clearly with intuitive explanations and doesn't overcomplicate them. The author simplifies ML jargon for anyone with basic computer or data knowledge. However, opinions differ on the pacing and quality of the book. Some find it solid and well-bound, while others report pages tearing or falling apart.
AI-generated from the text of customer reviews
Customers find the book provides good coverage of techniques and concepts in deep learning. It explains the topics clearly, providing a macro view of the topics needed to get started. The practical aspects and tips are invaluable, making it a valuable reference for self-study. The author has found a perfect balance between theory and application, making it a good reference for self-studying.
"...It is limited to deep learner but that’s why its called what it is. The author dabbles in other areas so the reader is aware of other things in AI...." Read more
"...The field of deep learning is really vast and Chollet covers an impressive amount in this book mostly at a relatively high/applied level, which I..." Read more
"...It rates 5 (or even 6!) stars for being an approachable introduction to Deep Learning, using the author's excellent Keras library to allow..." Read more
"...I especially like the chapter that talks about the functional API, where you can have multiple inputs, and multiple outputs, and layer weight..." Read more
Customers find the book provides clear, concise explanations of various classes of deep learning models. They appreciate the author's use of Python code to explain concepts and avoid overcomplicating the material. The book provides sufficient detail and logic to explain what is going on and why things are done. Readers find the book engaging, explaining challenging concepts conceptually while providing implementation.
"...I love Chollet's interpretation and explanations. I wish I could do the exercises but am having difficulty setting up the GPU machine...." Read more
"...Author just knows how to speak clearly, give information at the appropriate time, is well structured and still gives some very in dept info...." Read more
"...somehow able to make a textbook into a page turner, explaining challenging concepts conceptually while giving implementation examples...." Read more
"...It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers...." Read more
Customers have different views on the book's quality. Some find it solid with no crumbling, while others report pages tearing or falling apart.
"...As is, it is still pretty solid." Read more
"...I've had the book for four days, it is falling apart because it was cheaply produced, and I do not feel like the material I've gathered from it so..." Read more
"...No crumbles, good as new, safely delivered - even before the delivery due date! Very satisfied." Read more
"Good quality..." Read more
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Top reviews from the United States
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- Reviewed in the United States on May 9, 2020I have bought 10 books on ML/DL, and of those this is the 9th book that I have read (actually I have just started reading this book, but it's been so good thus far that I wanted to write a review.) As another reviewer noted, one should read other books on ML/DI to get a deeper understanding of the topic. This book explains using programs instead of using much mathematics. The advantage that I have had is my review of the same topics from other perspectives in books such as the following
Intro to statistical learning (by Hastie et al)
Intro to Machine Learning (by Alpaydin)
Deep Learning (by Goodfellow, Bengio etc)
Hands-on ML w SciKit, Keras and Tensorflow (by Geron)
When I first tried to read this book by Chollet in early April I was not as conversant with Python, and so I took a break and decided to brush up my limited Python knowledge by going through the first 6 chapters of "Automate the Boring Stuff with Python" (by Sweigert). Now that I have more knowledge of Python this book by Chollet is so much more comprehensible. As I said I have the advantage of having learned many of these concepts earlier. I love Chollet's interpretation and explanations. I wish I could do the exercises but am having difficulty setting up the GPU machine.
The problem I am dealing with with this book by Chollet is the setup of a GPU machine in the Amazon Cloud. If anyone can help me that would be greatly appreciated (I understand that this is not the forum to seek technical help on AWS, but I thought I'd give it a try)
- Reviewed in the United States on December 3, 2023This is probably the best into to Deep Learning one could get. Author just knows how to speak clearly, give information at the appropriate time, is well structured and still gives some very in dept info. It is limited to deep learner but that’s why its called what it is. The author dabbles in other areas so the reader is aware of other things in AI. Definitely a good starting point for someone with some programming chops but new to AI.
- Reviewed in the United States on May 19, 2022Read this cover to cover for my senior project and loved every minute of it, Francois Chollet was somehow able to make a textbook into a page turner, explaining challenging concepts conceptually while giving implementation examples. I also got the second addition and I would recommend using that one just so you are working through up-to-date examples with tensorflow/keras. The field of deep learning is really vast and Chollet covers an impressive amount in this book mostly at a relatively high/applied level, which I think is a good thing. There were a few of the later chapters I wish he went into more depth with, for the advanced computer vision chapter I really which he had touched on some more modern architectures like Mask- RCNN and other stuff
- Reviewed in the United States on June 8, 2018I'm a CS professor, and I chose this for my course in Deep Learning last term. Overall I am happy with the book, and will use it again. It rates 5 (or even 6!) stars for being an approachable introduction to Deep Learning, using the author's excellent Keras library to allow beginners to do remarkable work. My own class of undergrads was building DLNN models to do sophisticated image recognition tasks after just a few weeks.
So, why the four stars? Because the book is rather "paint by the numbers". The presentation is filled with "Now you'll do this.." followed by working blocks of code for the student to enter and run. But there are no exercises, code or mathematical. Even the standard backpropagation algorithm is only qualitatively described -- nice pictures of gradient descent in 2 dimensions, but no hard equations. (After all, Keras does it all for you, right?) And as the book ventures into more advanced areas like GANs, VAEs, etc the presentation is increasingly high-level and nonmathematical, providing only a feel for the topics without deep comprehension. Given the depth of the math involved, I suppose I can't blame Chollet for a bit of handwaving. But more rigor with deeper explanations would have been nice.
- Reviewed in the United States on September 21, 2018If you have taken some deep learning classes on Coursera, such as deeplearning.ai or fast.ai class, this book will serve as a refresher and a good tutorial to implement ideas in Keras. While it does not provide deep theoretical concepts, it explains enough to give you an understanding of what each layer does (conv1D, conv2D, LSTM, GRU, Dense, etc.) It also teaches about different ways to assemble the networks. I especially like the chapter that talks about the functional API, where you can have multiple inputs, and multiple outputs, and layer weight sharing. Most of the other books I read only talked about Sequential models. This book is not for you, if you are looking for mathematical explanations. It's perfect for someone who is not too interested in equations, and just want to have practical understanding.
- Reviewed in the United States on March 26, 2018I'm using this as the primary textbook for a Deep Learning course I'm designing right now for the University of Washington professional/continuing education program. I'll also assign readings from the Goodfellow et al. text, but Chollet's book is a more practical way to get started. He is also the author of the Keras framework; it's great to get advice "straight from the horse's mouth".
Overall this book is more about practical techniques and python code (in Keras) than about deep learning math/theory. This is probably what the majority of readers are looking for. It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers.
I would recommend complementing this book with two others:
1) as mentioned above: Deep Learning (Adaptive Computation and Machine Learning series)
2) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Top reviews from other countries
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David HernándezReviewed in Mexico on December 27, 2021
5.0 out of 5 stars Excelente libro
Llego tiempo y forma. Solo no me di cuenta que ya existe una segunda edición. Pero esta edición está bastante Completa.
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CFabioReviewed in Brazil on May 28, 2021
5.0 out of 5 stars Satisfeito com a compra
Ótimo livro. Fiquei muito satisfeito com a compra. Linguagem simples e de boa compreensão. Único ponto negativo é que ele é todo preto e branco. Não possui figuras coloridas.
- Tirthankar DuttaReviewed in India on August 23, 2023
5.0 out of 5 stars Perfect for learning deep learning from scratch with Keras library
The content is definitely great!! Keras is now the default wrapper not only for Tensorflow but also PyTorch. Hence, this book, whose focus is addressing deep learning from scratch using the Keras library, is a must for anyone who wishes to learn deep learning for advanced usage.
- BilalReviewed in Canada on June 21, 2019
5.0 out of 5 stars Should've been titled: Deep learning with the Keras framework and TensorFlow
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.
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AlexisReviewed in France on October 9, 2019
5.0 out of 5 stars Meilleur ouvrage sur le deep learning !
Ce livre est le meilleur pour s'initier et approfondir de deep learning ! Fier qu'il soit écrit par un français, François Chollet le développeur de Keras ! Avec Yann Lecun la star du machine learning chez Facebook et Aurélien Géron qui écrit également de très bon ouvrage pour approfondir le machine learning, les Français font des jaloux à l'international !! ;)