arogozhnikov Goto Github PK

3d_nn

Raymarching neural network in browsers with WebGL shaders

adamw_bfloat16

AdamW optimizer for bfloat16 models in pytorch 🔥.

agentnet

A lightweight library to build and train Deep Reinforcement Learning agents using Theano+Lasagne

arogozhnikov

arogozhnikov.github.io

'Brilliantly wrong' blog, Machine Learning visualizations live here

cats

Test of algorithms on highly-categorical data

cellxgene-gateway

Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.

cupy

NumPy & SciPy for GPU

deepmmd-gan

Yet another (very simple) approach for adversarial training.

demuxalot

Reliable, scalable, efficient demultiplexing for single-cell RNA sequencing

demuxlet

Genetic multiplexing of barcoded single cell RNA-seq

dt

eindex

Multidimensional indexing for tensors

einops

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

fakeleptons

FakeLeptons recognition for CMS experiment

gw150914

:wavy_dash::earth_africa::wavy_dash: Analyse GW150914 (gravitational waves)

hep_ml

Machine Learning for High Energy Physics.

incubator-mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

infiniteboost

InfiniteBoost: building infinite ensembles with gradient descent

iview5d

Extremely simplistic viewer of small ndimensional patches for jupyter notebook. Powered by einops

kronecker-attention-pytorch

Implementation of Kronecker Attention in Pytorch

micro-openfold

attempt to update openfold code to make it installable in 2024. Dropping binaries

microanarci

Minified version of ANARCI, pip-installable

microdockq

minified, pip-installable version of DockQ metric

mint

Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.

mlatgraddays2017

Machine Learning session on Grad Days at Heidelberg university 2017

mlatimperial2016

Materials for the course of machine learning at Imperial College organized by YSDA

mlatimperial2017

Materials for the course of machine learning at Imperial College organized by YSDA

mlhep2016

MLHEP 2016 materials

mlp-mixer-pytorch

An All-MLP solution for Vision, from Google AI