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.

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. 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.

Fastbook Reading & Discussion Session #12: Chapter 14 About: This will be a 2-hour weekly study group dedicated to going through the book, "Deep Learning for Coders with fastai and PyTorch: AI applications Without a PhD" written by the creators of the fast.ai Our Fastbook Sessions are led by Sanyam Bhutani. The authors of the course were kind enough to make an early draft available as Jupyter Notebooks. We will be going through these in the format: https://github.com/fastai/fastbook Note: To respect the license created by the authors, these sessions will not be recorded.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.