Deep Learning for Coders with Fastai and PyTorch
AI Applications Without a PhD
Book Details
| Authors | Jeremy Howard, Sylvain Gugger |
| Publisher | O'Reilly Media |
| Published | 2020 |
| Edition | 1 |
| Paperback | 624 pages |
| Language | English |
| ISBN-13 | 9781492045526, 9781492045519 |
| ISBN-10 | 1492045527, 1492045519 |
| License | GNU Free Documentation License |
Book Description
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.
This book is available under the GNU Free Documentation License, which means that you are free to copy, redistribute, and modify it, as long as you preserve all original copyright notices, provide transparent access to the source, and release any modified versions under the same license.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
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