Applying Tim Ferris's DISSS Metalearning system to learn and create documentation for the Fast.ai Deep Learning library
Note: There are many others who are creating comprehensive summaries of the Fast.ai lectures. I'm taking a far different approach than what others are doing, not that those lecture notes are not helpful, but I find it easier to organize my notes by topic than by lecture. For example, my Lesson 1 will contain material covered in Fast.ai's videos 1-3. I still strongly urge you to consult the Fast.ai lecture summaries written by the following people if you're still unsure of the material presented in them. I certainly used them in building my version personal version of the notes that I'm presenting here.
This repository is still Under Construction and will be updated frequently. It'll be organized into the following sections:
- Notes: This is where I write down any key or interesting findings from each Fast.ai lesson.
- Paperspace Protips: Tactics and shortcuts for running your Fast.ai code on Paperspace that wasn't covered in the course.
- Summaries (proposed): After completing the notes for each lesson, I'll create a 1-page summary for each lesson as per Tim Ferriss's DISSS system. I envision creating summaries similar to the Python cheat sheets on Datacamp.
- Helpers (proposed): This is where I'll write some helper functions to make some commonly performed routines even more efficient.