Popular GitHub Repositories for Graph Level Representation

Cross-platform, customizable ML solutions for live and streaming media.

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Graph Neural Network Library for PyTorch

MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone

WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

Netflix-level subtitle cutting, translation, alignment, and even dubbing - one-click fully automated AI video subtitle team | Netflix级字幕切割、翻译、对齐、甚至加上配音，一键全自动视频搬运AI字幕组

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Deep learning library featuring a higher-level API for TensorFlow.

BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.

Anomaly detection related books, papers, videos, and toolboxes

Reliable LLM Memory for AI Applications and AI Agents

a state-of-the-art-level open visual language model | 多模态预训练模型

Reading list for research topics in multimodal machine learning

FlowGram是一个基于节点的流程构建引擎，帮助开发者快速创建工作流程，支持固定布局和自由连接布局两种模式。

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models