Eugene Yan

applied-ml. 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

open-llms. 📋 A list of open LLMs available for commercial use.

ml-surveys. 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.

ml-design-docs. 📝 Design doc template & examples for machine learning systems (requirements, methodology, implementation, etc.)

obsidian-copilot. 🤖 A prototype assistant for writing and thinking

1-on-1s. 🌱 1-on-1 questions and resources from my time as a manager.

news-agents. 📰 Building News Agents to Summarize News with MCP, Q, and tmux

testing-ml. 🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.

llm-paper-notes. Notes from the Latent Space paper club. Follow along or start your own!

applyingml. 📌 Papers, guides, and mentor interviews on applying machine learning for ApplyingML.com—the ghost knowledge of machine learning.

papermill-mlflow. 🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.

python-collab-template. 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.

recsys-nlp-graph. 🛒 Simple recommender with matrix factorization, graph, and NLP. Beating the regular collaborative filtering baseline.

semantic-ids-llm. Semantic IDs: How to train an LLM-Recommender Hybrid with steerability and reasoning on recommendations.

align-app. TypeScript

visualizing-finetunes. Jupyter Notebook

fastapi-html. Sample repository demonstrating how to use FastAPI to serve HTML web apps.

eugeneyan. Python

framework-comparison. TypeScript

discord-llm. Experimenting with LLMs to Research, Reflect, and Plan (LLM assistants, retrieval, and Discord integration)

raspberry-llm. Calling LLM APIs on a Raspberry Pi for lulz

poc-docker-template. Simple template showing how to set up docker for reproducible data science with Jupyter notebooks.

text-to-image. Jupyter Notebook

my-cs-degree. A CS degree I designed for myself, 2020

learning-typescript. JavaScript

awesome-mlops. A curated list of references for MLOps

nocode-ml. 😝 End-to-end machine learning; "no code" required!

awesome-fastapi. A curated list of awesome things related to FastAPI

design-patterns. Java

deep-rl. Repository for deep reinforcement learning with OpenAI

testing-pipelines. Python

Computational-Thinking-and-Data-Science. edX: Introduction to Computational Thinking and Data Science (Oct 2014)

kaggle_springleaf. Code for Kaggle Springleaf Email Prediction Challenge

DeepLearningBook. MIT Deep Learning Book in PDF format

Mining-Massive-Datasets. Coursera: Mining Massive Datasets (Sep 2014)

ama. Ask Me Anything

search_fundamentals_course. Python

kaggle_titanic. Code for Kaggle Titanic Challenge (and other learning)

Computer-Science-and-Programming-In-Python. edX: Introduction to Computer Science and Programming in Python (July 2014)

search_engineering. Search Engineering course materials

openai-cookbook. Examples and guides for using the OpenAI API

datagene. Jupyter Notebook

Statistical-Inference. This repository contains the lab assignments for the facilitation of John Hopkins University' Coursera MOOC on Statistical Inference.

workshop. AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Data-Analysis-and-Statistical-Inference-Project. Coursera: Data Analysis & Statistical Inference Project (Feb 2014)

Statistical-Learning. Stanford OpenX: Introduction to Statistical Learning

search_with_machine_learning_course. Jupyter Notebook

Time-Series-Analysis. Simple forecasting with Regression Model

neural_networks_and_deep_learning.

Getting-and-Cleaning-Data. Coursera: Getting and Cleaning Data (May 2014)

Twitter-SMA. Twitter Streaming and Analysis with Python and R

evals. Evals is a framework for evaluating OpenAI models and an open-source registry of benchmarks.

Machine-Learning. Coursera: Machine Learning (Aug 2014)

json-to-utterances. Jupyter Notebook

xgboost. Large-scale and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, on single node, hadoop yarn and more.

awesome-self-supervised-learning. A curated list of awesome self-supervised methods

tensorflow-mnist. Tensorflow MNIST example using Dataset API

Visualizations. Random Visualizations