This repository is a transformation of the 2022 version of Fast.ai's Deep Learning for Coders Part 2. These code notebooks follow the notebooks from the first portion of the course. Please watch the Jeremy Howard's course videos for the complete context. In the course, Jeremy starts with some constraints. His approach is to first implement a concept in standard Python code. Once he's introduced the concepts, then the notebooks start to bring in PyTorch library code that also implements the concept. He tries to incrementally demystify the PyTorch and Fast.ai library code.
Similar to the course, these notebooks start from standard Elixir code and then bring in Nx and Axon libraries. We'll use Elixir and Livebook.dev interactive & collaborative code notebooks. A requirement for these notebooks is a running Livebook application or server. Livebook runs on Windows and Mac desktops and on Linux. Please see the Livebook web site for instructions on installing the basic Livebook application. Livebook also runs on Linux. For our purposes, we run Livebook on a local Linux server using escript. For more information on using Livebook on escript, please see the Readme.md at https://github.com/livebook-dev/livebook.
We'll be building a livebook for every Fast.ai Foundations Jupyter notebook. We welcome pull requests that improve our notebooks.