https://github.com/1587causalai/hung-yi-lee-genai-notes
结合书生·浦语参大模型实战营的内容， 系统化学习李宏毅老师 2024 年《生成式人工智能导论》课程。
https://github.com/1587causalai/stable-llm-service
稳定可靠的大模型调用中间层，支持多服务提供商策略和高并发控制
solvingmicrodsops
Christopher Carroll's Lecture Notes on Solving Microeconomic Dynamic Stochastic Optimization Problems and Indirect Inference
tcc-template
Template do Trabalho de Conclusão de Curso de Sistemas de Informação - EAD
guide-to-n3c-v1
Research with the National COVID Cohort Collaborative (N3C: https://ncats.nih.gov/n3c)
https://github.com/1587causalai/info-fusion-dpo
A Novel Alignment Approach based on Information Fusion View: Direct Preference Optimization with Dynamic Learnable β
https://github.com/1587causalai/medicalgpt-training-pipeline
训练定制大模型，实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO。
https://github.com/1587causalai/rlhf-reward-modeling-quickstart
Quick Start Recipes to train reward model for RLHF.
https://github.com/1587causalai/the-incredible-pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
IAMAP: Unlocking Deep Learning in QGIS for non-coders and limited computing resources
IAMAP: Unlocking Deep Learning in QGIS for non-coders and limited computing resources - Published in JOSS (2026)
https://github.com/1587causalai/awesome-causality-data
A data index for learning causality.
pmv-si-2024-1-pe3-t1-meupetviaja
pmv-si-2024-1-pe3-t1-meupetviaja created by GitHub Classroom
pcb-esp32-imu
PCB for the Surgery Robotics Project of the Robotics Course of the Biomedical Engineering Degree of the Universitat de Barcelona.
https://github.com/1587causalai/econml
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.