FastAI.jl documentation ideas

[lorenzoh]

Creating this as a place to gather ideas for documentation ideas for FastAI.jl as a way to organize but also to prioritize. Add any suggestions or ideas as a comment!

If possible, pieces of documentation should fall into one of these categories (see here for more details):

• tutorial: learning-oriented resource for users unfamiliar with the library or a concept
• how-to: straightforward instructions on using the library for x without too much background; focused on doing
• reference: technical documentation, including docstrings and module/API overviews
• background: more general discussions about implementation details or related concepts

Examples for each can be found in the current FastAI.jl documentation.

Ideas

• Tutorials
  • ❗ FastAI.jl for fast.ai users: Multi-part tutorial series to help fast.ai users get started with FastAI.jl
  • (Part 1) Julia Basics: Syntax basics, array programming
  • (Part 2) Flux.jl vs. PyTorch: Differences between the frameworks, code comparisons for building a model
  • (Part 3) FastAI.jl vs. fast.ai: Differences shown by comparing the code for a basic finetuning task. Pointers to more resources.
  • Using parts of the API separately: Explains how FastAI.jl is built on many decoupled packages and that you don't have to use all of them. For example, showing how to use the LearningMethod machinery with a regular Flux.jl training loop and, inversely, using a Learner but with a custom data iterator and no learning method.
  • Serving predictions on a web server: Reuses the trained model from the serialization tutorial and shows how to package it into a small HTTP server that can be used to get predictions.
  • Implementing callbacks: Go from using callbacks to implementing your own callbacks, and explore how several existing callbacks are implemented. (Basic version here)
  • Siamese image similarity: Showcase different parts of FastAI.jl's APIs to implement an image similarity learning task (original fast.ai tutorial), FastAI.jl#31
  • Progressive resizing: Explain the method and implement it by building on the presizing tutorial. Train a vision model using it.
  • Transfer learning: Explain transfer learning, backbones, pretrained models and the techniques used to successfully finetune them.
• How-to
  • Implement callbacks: Checklist for implementing callbacks.
  • Evaluate models: Measuring performance on trained models
• Reference
  • FastAI.jl vs. fast.ai cheatsheet: Compare concepts and their equivalents in both libraries.
  • Packages: Overview of packages that FastAI.jl depends on for different parts of its API: Flux.jl, DLPipelines.jl, DataAugmentation.jl, DataLoaders.jl, Metalhead.jl, ...

[darsnack]

@lorenzoh is there anything you want to transfer here?

[lorenzoh]

Closing in favor of https://github.com/FluxML/FastAI.jl/issues/204