MLEngineerSummary
MLEngineerSummary copied to clipboard
montali
Everything a Machine Learning Engineer needs to know, from statistics, probability theory, ML, DL and AI.
ML Engineer Summary
This started as an interview cheatsheet, but it is slowly becoming a bible for ML engineers. You can find some basics of probability theory, statistics, Python, Machine Learning, Deep Learning and Reinforcement Learning.
How to read this
It is crucial to open this with either Typora or Visual Studio Code: normal markdown editors won't parse the LaTeX formulas.
Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
Distributed under the GPL License. See LICENSE for more information.
Hire me!
I'm currently looking for AI positions :) Add me on LinkedIn! Simone Montali
montali