Starred repositories
A community-maintained Python framework for creating mathematical animations.
A beautiful resume/cover letter LaTeX template pair that are extraordinarily easy to use.
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 2…
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
Latex code for making neural networks diagrams
Impress your boss and turn a Jupyter notebook into a beautiful, shareable web app — no callbacks, no frontend, no rewrite.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and mo…
Real time code sharing for your lectures and presentations.
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
PyTorch Tutorial for Deep Learning Researchers
newspaper3k is a news, full-text, and article metadata extraction in Python 3. Advanced docs:
Python library for converting Python calculations into rendered latex.
Best Practices, code samples, and documentation for Computer Vision.
End-to-end training of sparse deep neural networks with little-to-no performance loss.
Code for the paper "Training Binary Neural Networks with Bayesian Learning Rule
Last-layer Laplace approximation code examples
Belief matching framework official implementation
A collection of resources for socially engaged Data Science & Statistics
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive lea…
Google Research
A highly efficient implementation of Gaussian Processes in PyTorch
Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX