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agentnet

Deep Reinforcement Learning library for humans

aleph2015

Applying (machine) Learning to Experimental Physics (ALEPH) and «Flavours of Physics» challenge

algorithms

Тут находится страница курса алгоритмов yandexdataschool.ru

anytask

asml

calogan

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

cern-higgsml-baseline

baseline solution for HiggsML challenge using data from CERN open data portal

cern_summer_school_2017

CERN openlab Summer School 2017 Machine Learning - Parts 3 &4

cms-dqm

CMS data quality monitoring

comet-example-ci

DevOps example on COMET tracking dataset

comet-example-raw

coursera-dl-env

Environment for those who wish to run the Introduction to Deep Learning course materials locally

crayimage

A toolkit for image manipulation. Not for humans.

crowd_course

csc_deeplearning

3-day dive into deep learning at csc

darkmatter-2017

datanight2015-advanced-starterkit

Data Analysis Night Advanced StarterKit (https://academy.yandex.ru/events/data-analysis-night/2015/)

datanight2015-starterkit

Data Analysis Night StarterKit (https://academy.yandex.ru/events/data-analysis-night/2015/)

datapopularity

Storage optimization for LHCb experiment.

deep_vision_and_graphics

Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

dl-course-tensorflow

[in progress] Translating all our materials from https://github.com/ddtm/dl-course to tensorflow

dlatscale_draft

This is an early version of Deep Learning at Scale course for Yandex School of Data Analysis

dqn_binder

a deep reinforcement learning tutorial

dt

dubna_dl

dubna_docker

Dockerfile for deep learning tutorial on https://indico.cern.ch/event/452159/other-view?view=standard

escience-2016-everware

The presentation for https://www.esciencecenter.nl/event/4th-national-escience-symposium

everware

Everware is about re-useable science, it allows people to jump right in to your research code.

everware-base-image

fastdirc

Code used to generate the data for Derkach, D., Kazeev, N., Ratnikov, F., Ustyuzhanin, A., & Volokhova, A. (2020). Cherenkov detectors fast simulation using neural networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 952, 161804.