
Show HN: Machine learning flight rules; tips and tricks for ml - 16yoMLDev
https://github.com/bkkaggle/machine-learning-flight-rules
======
16yoMLDev
A guide for astronauts (now, people doing machine learning) about what to do
when things go wrong.

GitHub: [https://github.com/bkkaggle/machine-learning-flight-
rules](https://github.com/bkkaggle/machine-learning-flight-rules)

Product Hunt: [https://www.producthunt.com/posts/machine-learning-flight-
ru...](https://www.producthunt.com/posts/machine-learning-flight-rules)

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](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/](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](http://amid.fish/reproducing-deep-rl))

