https://news.ycombinator.com/item?id=31874308
Some that looked interesting:
* https://learngitbranching.js.org/
* https://github.com/blog/2019-how-to-undo-almost-anything-wit...
Before I had a good mental model of git, I would've been comfortable with flows like 'add, commit, push' (and the smallest amount of branching from that; 'checkout -b', 'pull'). -- I wasn't able to easily dig myself out of holes I got into if I made the wrong command.
The repo "git flight rules", which explains how to fix problems you get into, has 33k stars. https://github.com/k88hudson/git-flight-rules
Leaving this here for reference.
GitHub: https://github.com/bkkaggle/machine-learning-flight-rules
Product Hunt: https://www.producthunt.com/posts/machine-learning-flight-ru...
There's a lot of "hidden knowledge" online on places like Stackoverflow, Kaggle, and the Pytorch discussion forums that is really useful but not easily accessible to people who are just getting started with machine learning. This is why I made Machine learning flight rules, this Github repo compiles all of the things I have learned over the last two years about best practices, common mistakes, and little-known tricks when training neural networks. I've tried to make sure that all the information in this repository is accurate, but if you find something that you think is wrong, please let me know by opening an issue. This repository is still a work in progress, so if you find a bug, think there is something missing, or have any suggestions for new features, feel free to open an issue or a pull request. Feel free to use the library or code from it in your own projects, and if you feel that some code used in this project hasn't been properly accredited, please open an issue. I named this project after the awesome Git Flight Rules project (https://github.com/k88hudson/git-flight-rules). I took a lot of tips from both Andrej Kaparthy's blog post on a recipe for training neural networks (https://karpathy.github.io/2019/04/25/recipe/) and the Amid Fish blog post on lessons learned when reporoducing a deep reinforcement learning paper (http://amid.fish/reproducing-deep-rl)