機械学習 · GitHub

AI Healthcare System & Clinical Data Platform. Combines a Databricks Medallion Lakehouse (OHDSI OMOP CDM v5.4 & FHIR R4) with TabICLv2 tabular foundation models, calibrated tree ensembles (CatBoost, XGBoost), a 10-year coupled ODE digital twin simulator, LangGraph multi-agent RAG, and an offline HIPAA-compliant clinical decision support portal.

High performance distributed framework for training deep learning recommendation models based on PyTorch.

An All-in-One Algo-Trading Framework: Backtest -> Train -> Trade -> Monitor. Machine / Deep Learning Ready. Supports All Trading: TradFi+CeFi+DeFi. Code Once, Trade Anywhere.

• Supports vectorized and event-driven backtesting with different resolutions of data, e.g. tick data, second data and minute data etc.
• Dataframe-centric trading workflow

PixlStash helps you find things in an image library that's gotten out of hand. It imports and tags your images automatically, then lets you search by content or face, sort the keepers from the rubbish, and serve data to other tools (like ComfyUI) with a REST API. Use the desktop version or run it headless as a server.

人間のためのトピックモデリング

• All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core),
• easy to plug in your own input corpus/datastream (trivial streaming API)

[NeurIPS2021] A plant image dataset with high label ambiguity and a long-tailed distribution

圧倒的な優位性をもたらす研究エンジンです。他の人が1つの取引アイデアを検証し終える前に、数千ものアイデアを検証できます。

• Fast, vectorized backtesting Research strategies at scale with pandas, NumPy, and Numba. Backtesting examples → - Optional Rust engine Use precompiled kernels for supported operations without JIT compilation. Rust engine →

Large datasets for conversational AI

Opensource Python project for cancer radiation treatment planning [NeurIPS'26, AAPM'23]

Wav2Lip version 288 and pipeline to train

This repository contains an exhaustive coverage of a hands on approach to PyTorch along side powerful tools to accelerate model tuning and training

ONNXim is a fast cycle-level simulator that can model multi-core NPUs for DNN inference

Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.

Code and information for face image quality assessment with SER-FIQ

[参赛队伍] TAAC 2026 腾讯广告算法大赛 X KDD 2026

PyC (Pytorch Concepts) is a PyTorch-based library for designing concept-based interpretable deep learning models.