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1day-notebooks

abk_dl_2019

active_sciencing

active learning + reusable workflows + likelihood free inference

adc2021-examplecode

adversarial-jets

Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

ai_physicist

AI Physicist, a paradigm with algorithms for learning theories from data, by Wu and Tegmark (2019)

analisi-dati

Luca Lista Statistics Course ( Univ. Naples )

anomalies

anomalydetection

avikar-surf2017

Some of the summer work

awesome-hep

A curated list of awesome high energy and particle physics software.

bari_lectures

btag_l1

L1 development for BTagging

calogan

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

calographnn

camlab-dlsctutorials

carl

Likelihood-free inference toolbox.

cgnet

clear-diffusion-keras

Implementation of denoising diffusion models with schedules, improved sampling, and other extensions using Keras.

clscraper

A scraper for craigslist that will email you everytime a new listing is posted.

cms_18_120_ancodes

Macros from CMS AN-18-120

coffea

Basic tools and wrappers for enabling not-too-alien syntax when running columnar Collider HEP analysis.

coffeamaker

Spark analyzer for NanoAOD coffea powder

course2020

data_science_course_ws_2023_2024

dctools

Combine datacard maker, signal extraction, statistical analysis and unfolding

dctr

deep-learning-physics

This project contains additional material for the textbook Deep Learning for Physics Research by Martin Erdmann, Jonas Glombitza, Gregor Kasieczka, and Uwe Klemradt.

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

deep-learning-with-python-notebooks

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