Starred repositories
12 Lessons to Get Started Building AI Agents
Black-box Bayesian inference for agent-based models
OpenABM-Covid19: an agent-based model for modelling the spread of SARS-CoV-2 (coronavirus) and control interventions for the Covid-19 epidemic
all of the workflows of n8n i could find (also from the site itself)
The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation…
Generate audiobooks from e-books, voice cloning & 1158+ languages!
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Multi-Agent Reinforcement Learning (MARL) papers
数学建模和机器学习/深度学习/大模型的笔记和资料（持续更新中......）。
KAG开源框架介绍及使用KAG实现知识增强生成应用(产品模式测试、开发者模式测试)，KAG是OpenSPG发布v0.5版本中推出的知识增强生成（KAG）的专业领域知识服务框架，旨在充分利用知识图谱和向量检索的优势，增强大型语言模型和知识图谱，以解决 RAG 挑战
Library for Bayesian inference using sequential Monte Carlo (particle filter, condensation, genetic, ...) methods.
Implementation of particle filtering and exact inference for random Dynamic Bayesian Networks (DBNs), including random query selection, parent subset conditioning, simulated evidence, and result vi…
multi-agent deep reinforcement learning for large-scale traffic signal control.
Practical applications towards risk-centric portfolio management
Code and experiments for *BERTopic: Neural topic modeling with a class-based TF-IDF procedure*
Probabilistic reasoning over time using HMMs, DBNs, and particle filtering
Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system utility,…
A easy HMM program written with Python, including the full codes of training, prediction and decoding.
SEIR model on a graph, to simulate covid-19 spread.
PyMC3 implementation of Drew Linzer’s dynamic Bayesian election forecasting model