Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
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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.
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Product details
Especificacions
| Main Specification | |
|---|---|
| Marca | by Jeremy Howard (Author), Sylvain Gugger (Author) Format: Paperback |
| Data de publicació | 2020, August 25 |
| Pes de l' element | 2, 15 lliures |
| Product information | |
|---|---|
| Idioma | Anglès |
| Altres | |
|---|---|
| Tipus de producte | Neural Networks |
| Edició | 1r |
| Editor | O'Reilly Media |
| Longitud d' impressió | 621 pages |
| ISBN 10 | 1492045527 |
| ISBN- 13 | 978-1492045526 |
| Dimensions | 7.25 x 1 x 9.25 inches |
| ISBN10 | 1492045527 |
| ISBN13 | 978-1492045526 |
| Etiquetes | |
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