Stars
This repository contains all the notebooks used for the Machine Learning for Fundamental Physics (ML4FP) School 2024
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
Examples and guides for using the OpenAI API
Tools and tests used in Kaggle Learn exercises
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Symmetry Preserving Attention Networks for Event Reconstruction
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
The fastai book, published as Jupyter Notebooks
Auto-launching the RISE plugin for Binder presentations.
Code and Data for the paper "Improving Parametric Neural Networks for High-Energy Physics (and Beyond)" MLST 2022, at https://doi.org/10.1088/2632-2153/ac917c.
Hadronic Interaction Model interface in PYthon
Exercise on Anomaly Detection in Particle Physics for the 3rd Terascale School of Machine Learning