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Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Paperback – Aug. 25 2020
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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.
Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.
- Train models in computer vision, natural language processing, tabular data, and collaborative filtering
- Learn the latest deep learning techniques that matter most in practice
- Improve accuracy, speed, and reliability by understanding how deep learning models work
- Discover how to turn your models into web applications
- Implement deep learning algorithms from scratch
- Consider the ethical implications of your work
- Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
- ISBN-101492045527
- ISBN-13978-1492045526
- Edition1st
- PublisherO'Reilly Media
- Publication dateAug. 25 2020
- LanguageEnglish
- Dimensions17.53 x 2.79 x 23.11 cm
- Print length621 pages
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From the Publisher

From the Preface
Deep learning is a powerful new technology, and we believe it should be applied across many disciplines. Domain experts are the most likely to find new applications of it, and we need more people from all backgrounds to get involved and start using it.
That’s why Jeremy cofounded fast.ai, to make deep learning easier to use through free online courses and software. Sylvain is a research engineer at Hugging Face. Previously he was a research scientist at fast.ai and a former mathematics and computer science teacher in a program that prepares students for entry into France’s elite universities. Together, we wrote this book in the hope of putting deep learning into the hands of as many people as possible.
Who This Book Is For
If you are a complete beginner to deep learning & machine learning, you are most welcome here. Our only expectation is that you already know how to code, preferably in Python. If you are already a confident deep learning practitioner, you will also find a lot here.
In this book, we will be showing you how to achieve world-class results, including techniques from the latest research. As we will show, this doesn’t require advanced mathematical training or years of study. It just requires a bit of common sense and tenacity.
No Experience? No Problem!
If you don’t have any experience coding, that’s OK too! The first three chapters have been explicitly written in a way that will allow executives, product managers, etc. to understand the most important things they’ll need to know about deep learning. When you see bits of code in the text, try to look them over to get an intuitive sense of what they’re doing. We’ll explain them line by line. The details of the syntax are not nearly as important as a high-level understanding of what’s going on.
What You Need to Know
As we said before, the only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course. It doesn’t matter if you remember little of it right now; we will brush up on it as needed. Khan Academy has great free resources online that can help.
We are not saying that deep learning doesn’t use math beyond high school level, but we will teach you (or direct you to resources that will teach you) the basics you need as we cover the subjects that require them.
The book starts with the big picture and progressively digs beneath the surface, so you may need, from time to time, to put it aside and go learn some additional topic (a way of coding something or a bit of math). That is completely OK, and it’s the way we intend the book to be read. Start browsing it, and consult additional resources only as needed.
Please note that Kindle or other ereader users may need to double-click images to view the full-sized versions.
Product description
About the Author
Jeremy’s most recent startup, Enlitic, was the first company to apply deep learning to medicine, and has been selected one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was previously the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.
He has many television and other video appearances, including as a regular guest on Australia’s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.
Sylvain is a former teacher and a Research Scientist at fast.ai, with a focus on making deep learning more accessible by designing and improving techniques that allow models to train fast on limited resources.
Prior to this, Sylvain wrote several books covering the entire curriculum he was teaching in France (published at Éditions Dunod) until 2015 in CPGE. CPGE are a French specific two-year program whereby handpicked students who graduated high school follow an intense preparation before sitting for the competitive exam to enter the top engineering and business schools of the country. Sylvain taught computer science and mathematics in that program for seven years.
Sylvain is an alumni from École Normale Supérieure (Paris, France) where he studied mathematics and has a Master’s Degree in mathematics from University Paris XI (Orsay, France).
Product details
- Publisher : O'Reilly Media; 1st edition (Aug. 25 2020)
- Language : English
- Paperback : 621 pages
- ISBN-10 : 1492045527
- ISBN-13 : 978-1492045526
- Item weight : 100 g
- Dimensions : 17.53 x 2.79 x 23.11 cm
- Best Sellers Rank: #13,203 in Books (See Top 100 in Books)
- #3 in Graphics & Visualization Textbooks
- #3 in AI Theory of Computing
- #4 in Machine Theory
- Customer Reviews:
About the authors
I'm a data scientist, researcher, developer, educator, and entrepreneur. I am a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible, and am a Distinguished Research Scientist at the University of San Francisco, am the chair of WAMRI, and am the Chief Scientist at platform.ai.
I have a young daughter, and live in San Francisco, after spending most of my life in Australia. You might have seen me on TV during my brief period of fame as the co-founder of the global Masks4All movement.
I'm a Research Engineer at Hugging Face, a company focusing in making the newest NLP models as to use as possible. Previously a Research Scientist at fast.ai, an institute dedicated to making deep learning more accessible.
Before that, I was a math and Computer Science teacher in CPGE in France.
I live in Brooklyn with my husband and two sons.
Customer reviews
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Top reviews
Top reviews from Canada
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Before discovering this gem, I struggled to grasp AI development through other courses. They left me lost in a sea of complicated syntax and discouraged by unworkable sample code. However, everything changed when I found FastAI's course and this accompanying book.
Within the first hour, I built an AI model to classify images, thanks to FastAI's accelerator that condenses intricate coding into just 10 to 20 essential lines. This was a game-changer for me, as it empowered me to deconstruct the code, run it, and experiment with my own variations.
The book's brilliance lies in breaking down complex concepts into digestible details. It offers a perfect blend of hands-on learning and theory, ensuring you never get lost in pages of abstract concepts. Some standout features include:
• Practical, guided lessons that keep you engaged and learning by doing.
• The perfect balance of theory, strategically placed to enhance understanding.
• An accessible approach for those with a coding background and basic Python knowledge.
In the era of ChatGPT, you might wonder if this book is still relevant. I believe it's even more crucial now! As you use ChatGPT to expedite FastAI development, this book equips you with the strategic insight to harness the full potential of generative AI tools by understanding the underlying technology at a granular level. For instance, you'll learn:
• The importance of separate validation data sets distinct from design/test data.
• The significance of key parameters and their implications.
• In-depth AI knowledge that helps you conceptualize and communicate your ideas effectively.
Don't let AI development intimidate you any longer. FastAI's book is your key to unlocking the exciting world of AI, giving you the tools, knowledge, and confidence to thrive in this cutting-edge field. Embark on your AI journey today and experience the transformation for yourself!
Top reviews from other countries





Reviewed in India 🇮🇳 on October 24, 2020


Book is simply superb and forms a much needed single source of structured content backing the fastai course. Video lectures are great, but after whetting ones appetite they leave you hankering for something more, that's exactly what this book is.



Reviewed in Mexico 🇲🇽 on October 27, 2020

