toufunao

toufunao

Geek Repo

Company:CFETSIT

Location:Shanghai,China

Github PK Tool:Github PK Tool

toufunao's repositories

Language:PythonStargazers:33Issues:1Issues:0

autogen

A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap

Language:Jupyter NotebookLicense:CC-BY-4.0Stargazers:0Issues:0Issues:0

Awesome-Federated-Learning

Federated Learning Library: https://fedml.ai

Stargazers:0Issues:1Issues:0

DeepLearning-1

深度学习入门教程, 优秀文章, Deep Learning Tutorial

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:0Issues:0Issues:0

DySAT

Representation learning on dynamic graphs using self-attention networks

Stargazers:0Issues:0Issues:0

FedGraphNN

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

Language:PythonStargazers:0Issues:1Issues:0

FedML

A Research-oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation. Best Paper Award at NeurIPS 2020 Federated Learning workshop. Join our Slack Community:(https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w)

License:Apache-2.0Stargazers:0Issues:0Issues:0

flower

Flower - A Friendly Federated Learning Framework

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0
Language:PythonStargazers:0Issues:0Issues:0

generative-ai-for-beginners

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0

graph-of-thoughts

Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models"

Language:PythonLicense:NOASSERTIONStargazers:0Issues:0Issues:0

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

Stargazers:0Issues:0Issues:0

graphSAGE-pytorch

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

Stargazers:0Issues:0Issues:0

graphsage-simple

Simple reference implementation of GraphSAGE.

Language:PythonStargazers:0Issues:0Issues:0

LLMs-from-scratch

Implementing a ChatGPT-like LLM from scratch, step by step

Language:Jupyter NotebookLicense:NOASSERTIONStargazers:0Issues:0Issues:0

Machine-Learning-for-Algorithmic-Trading-Second-Edition

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

Stargazers:0Issues:0Issues:0

minGPT

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0

paper-reading

深度学习经典、新论文逐段精读

License:Apache-2.0Stargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

pic_repo

图床

Stargazers:0Issues:0Issues:0

ReAct

[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models

License:MITStargazers:0Issues:0Issues:0

reflexion

Reflexion: an autonomous agent with dynamic memory and self-reflection

License:MITStargazers:0Issues:0Issues:0

reflexion-human-eval

An implementation of a Reflexion agent for SOTA Human-Eval Python results.

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

SuperAdapters

Finetune ALL LLMs with ALL Adapeters on ALL Platforms!

License:Apache-2.0Stargazers:0Issues:0Issues:0

system-design

Learn how to design systems at scale and prepare for system design interviews

License:GPL-3.0Stargazers:0Issues:0Issues:0

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

License:NOASSERTIONStargazers:0Issues:0Issues:0

THGNN

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

Language:PythonLicense:GPL-3.0Stargazers:0Issues:0Issues:0

torch-rechub

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

License:MITStargazers:0Issues:0Issues:0
Language:HTMLLicense:MITStargazers:0Issues:0Issues:0