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🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth!
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
POT : Python Optimal Transport
Make videos on the computer you already own. FreeVideo runs MiniMax H3 in as little as 8 GB of VRAM and 16 GB of RAM, and adapts its acceleration path to your hardware.
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
A curated list of gradient boosting research papers with implementations.
🚧 Under construction — no-training RefMod reference adapters for MiniMax H3 (compress reference images/videos into tiny latents, no training needed).
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Neural networks toolbox focused on medical image analysis
Reference-image character and style transfer for Anima in ComfyUI (offset-RoPE reference frame + LoRA)
Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
Official implementation of "AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers"
AFI VISION BUILDER
Trust metadata for AI agents — a stamp costs ~15 tokens, re-verifying costs 15,000. Agents stamp what they verify; the next agent runs 'akf check' and builds on it. pip install akf
A Python framework that uses machine learning algorithms to implement the metadata recovery attack against obfuscated programs.
Panoptic segmentation algorithms for 2D and 3D electron microscopy images