cwehmeyer

cwehmeyer's Stars

• tensorflow/tensorflow

  An Open Source Machine Learning Framework for Everyone

  Language:C++184k7.6k39.5k74.1k

• ggerganov/llama.cpp

  LLM inference in C/C++

  Language:C++62.6k5253.5k9k

• CSSEGISandData/COVID-19

  Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

  29.1k8653.8k18.5k

• taichi-dev/taichi

  Productive, portable, and performant GPU programming in Python.

  Language:C++25.1k3892.6k2.3k

• dennybritz/reinforcement-learning

  Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

  Language:Jupyter Notebook20.3k8601556k

• auchenberg/volkswagen

  :see_no_evil: Volkswagen detects when your tests are being run in a CI server, and makes them pass.

  Language:JavaScript14.1k9533234

• karpathy/micrograd

  A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

  Language:Jupyter Notebook9.6k147291.4k

• rtqichen/torchdiffeq

  Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

  Language:Python5.4k127214910

• tensorspace-team/tensorspace

  Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js

  Language:JavaScript5k134200441

• zedr/clean-code-python

  :bathtub: Clean Code concepts adapted for Python

  Language:Python4.3k16414763

• higgsfield-ai/higgsfield

  Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

  Language:Jupyter Notebook3.3k781554

• scikit-optimize/scikit-optimize

  Sequential model-based optimization with a `scipy.optimize` interface

  Language:Python2.7k63647548

• google-deepmind/mathematics_dataset

  This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty.

  Language:Python1.8k6713244

• pytorch/opacus

  Training PyTorch models with differential privacy

  Language:Jupyter Notebook1.7k43286329

• dhaitz/mplcyberpunk

  "Cyberpunk style" for matplotlib plots

  Language:Python1.7k172468

• FoldingAtHome/coronavirus

  Folding@home COVID-19 efforts

  Language:Python1.1k703680

• karpathy/randomfun

  Notebooks and various random fun

  Language:Jupyter Notebook1.1k464128

• deeptime-ml/deeptime

  Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

  Language:Python732207281

• rtqichen/ffjord

  code for "FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models".

  Language:Python620280139

• computationalmodelling/nbval

  A py.test plugin to validate Jupyter notebooks

  Language:Python4371110951

• jupyter4edu/jupyter-edu-book

  Teaching and Learning with Jupyter

  Language:TeX4284946130

• box/flaky

  Plugin for nose or pytest that automatically reruns flaky tests.

  Language:Python3741810260

• facebookincubator/flowtorch

  This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a community of users and contributors by focusing initially on complete infra and documentation for how to use and create components.

  Language:Jupyter Notebook301302721

• rrthomas/hpmor

  PDF, ePUB and Mobi versions of “Harry Potter and the Methods of Rationality”, from LaTeX source

  Language:TeX291107153

• choderalab/software-development

  A primer on software development best practices for computational chemistry

  256267576

• MobleyLab/basic_simulation_training

  A document for the Living Journal of Computational Molecular Science (LiveCoMS) which describes basic training for molecular simulations (oriented towards molecular dynamics (MD)), providing some training itself and linking out to other helpful information elsewhere. The intent is that this provide information on the prerequisites which will be required for understanding/following many of the other "best practices" documents being prepared.

  Language:TeX103214417

• statice/awesome-synthetic-data

  A curated list of awesome synthetic data tools (open source and commercial).

  753112

• statice/anonymeter

  A Unified Framework for Quantifying Privacy Risk in Synthetic Data according to the GDPR

  Language:Python6491415

• jchodera/statmech-for-biochemists

  A primer on statistical mechanics for biochemists

  Language:TeX45834

• amardt/DeepGenMSM

  Code for the results of the Paper:

  Language:Jupyter Notebook217010