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[P] Machine Learning Flight Rules

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

Product Hunt: 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). 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)

submitted by /u/16yoMLDev
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.