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A series of lectures and hands-on exercises on use of JAX in HEP analysis.

Invertible Neural Networks For Extracting Results

Living Review of Machine Learning for Particle Physics

Notebooks for introduction to statistics course

Fast, lightweight and parallelised simulation-based inference in JAX.

Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

TorchCFM: a Conditional Flow Matching library

Integration test allowing to test network and I/O for data analysis

Python training for business analysts and traders

clad -- automatic differentiation for C/C++

RooFit Standalone Development Repository

OS X frontend for inspire-hep and arxiv

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

💻 Ability to rename desktop spaces on macOS 10.10+

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Croissant is a high-level format for machine learning datasets that brings together four rich layers.

Normalizing flows with pytorch

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

Quantum Annealing for distribution unfolding in experimental High-Energy Physics

Definition of the HEP statistics serialization standard (HS3)

Automatic differentiation for high-energy physics correction factor calculations.

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Tutorials on computing essentials for HEP