I'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.
ISBN-13: 978-1617294433
ISBN-10: 9781617294433
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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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| Deep Learning with Francois Chollet |
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Customer reviews
I've just received this book. I usually don't write reviews on products that I purchase. However, i couldn't let this one pass unnoticed to the readers. Though the book content is great as you can see from most of the reviews, the book quality is a huge disappointment. The paper, pictures, print are of very bad quality! I can barely see what is on the figures! A massive waste of money. I've bought a lot of books in my life. This one has been the worst in terms of quality.
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Top reviews from the United States
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Reviewed in the United States on June 8, 2018
Great way to get started with Deep Learning; a very practical and up-to-date (early 2018) guide from the creator of Keras
Reviewed in the United States on March 26, 2018Reviewed in the United States on March 22, 2018
Reviewed in the United States on May 9, 2020
Reviewed in the United States on January 2, 2018
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, 2018Mr. Francis A. King
An excellent book. You will learn much.
Reviewed in the United Kingdom on June 27, 2019Amazon Customer
Fake Book, Cheating customers.
Reviewed in India on April 14, 2019
Fake Book, Cheating customers.
Reviewed in India on April 14, 2019
Fake book provided by Amazon.They are cheating there customers.All images and charts are black and white.Its just fake print of the book
Reviewed in India on April 14, 2019
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hfffoman
Practical and easy to learn from
Reviewed in the United Kingdom on November 13, 2018AndyB
Wondering where the Kindle version is? Wonder no more!
Reviewed in the United Kingdom on May 14, 2018
