Machine Learning · GitHub

A curated list of MLOps references

A Cloud Native Batch System (Project under CNCF)

零基础入门大模型（LLM）的基础教程：从零手写 GPT-2，一行行看懂大模型是怎么工作的 | Build GPT-2 from scratch in PyTorch, step by step

Easy Docker setup for Stable Diffusion with user-friendly UI

:memo: An awesome Data Science repository for learning and applying data science to real-world problems.

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

VIP cheatsheets for Stanford's CS 229 Machine Learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

Visual Novel Character Creation Suite is a comprehensive tool for creating character sprites for visual novels. It allows you to create unique characters with a consistent appearance across all images, which was previously a challenging task when using neural networks.

Publication-ready NN-architecture schematics.

Parametric differentiable curves with PyTorch for continuous embeddings, shape-restricted models, or KANs

• Differentiable: Custom autograd function ensures gradients flow properly through the curve evaluation.
• Vectorized: Vectorized operations for efficient batch and multi-curve evaluation.

Roadmap to becoming an Artificial Intelligence Expert in 2022

A collection of CVPR 2026 papers and open-source projects

Lightweight & fast OCR models for license plate text recognition.

• Keras 3 Backend Support: Train seamlessly using TensorFlow, JAX, or PyTorch backends 🧠
• Efficient Execution: Lightweight models that are cheap to run 💰

The best performing voice isolator there is. Keeps the caller's voice and removes the other voices in the room before speech-to-text hears them. Open source, streaming, runs on CPU and in the browser. pip install voxlock

As the core component of the Ascend for PyTorch community, TorchNPU is a deep learning adapter plugin built by Ascend for PyTorch, enabling the PyTorch framework to directly invoke Ascend NPU and providing developers with the powerful compute capabilities of Ascend AI processors. (About Ascend NPU: Ascend NPU (Neural Processing Unit) is a series of

• Basic Compute: Extensive support for PyTorch native APIs and custom APIs, covering mainstream AI scenarios with a consistent experience, enabling rapid model and algorithm implementation.
• Distributed: Supports accelerating distributed training via FSDP2. Core compute APIs support DTensor, along with collective communication primitives such as AllGather, AllReduce, and AllToAll, as well as point-to-point primitives like Send and Recv.

This is Andrew NG Coursera Handwritten Notes.