Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Power systems optimization course materials

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Code for the paper "Language Models are Unsupervised Multitask Learners"

This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

Implementation of Reinforcement Learning algorithms in Python, based on Sutton's & Barto's Book (Ed. 2)

https://cs330.stanford.edu/

Contains the code of the 1st place submission to the 2022 Learning 2 Run a Power Network (L2RPN) Network Competition.

Source code for the influence model based failure cascade prediction and analysis

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Dedicated Resources for the Low-Level System Design. Learn how to design and implement large-scale systems. Prep for the system design interview.

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

Competitive programming questions.

Notes and tutorials on how to use python, pandas, seaborn, numpy, matplotlib, scipy for data science.

A curated list of awesome Machine Learning frameworks, libraries and software.

Taking notes as I read through Elements of Programming Interviews in Python by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash.

Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

this is a collection of books and courses for machine learning.

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

This repository provides everything you need to get started with Python for (social science) research.

A set of random and arbitrary tools and games I've created to help explain control theory.

Practical notebooks for Khipu 2019, held in Universidad de la República in Montevideo.

Google's Engineering Practices documentation