kaitokuroba7

kaitokuroba7's Stars

• jgraph/drawio-desktop

  Official electron build of draw.io

  Language:JavaScript50.5k5281.5k5k

• Binaryify/NeteaseCloudMusicApi

  网易云音乐 Node.js API service

  30.3k3611.3k15.8k

• jindongwang/transferlearning

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

  Language:Python13.4k3393393.8k

• thuml/Time-Series-Library

  A Library for Advanced Deep Time Series Models.

  Language:Python6.9k725061.1k

• xxlllq/system_architect

  :100: 2024年系统架构设计师（软考高级）备考资料。

  Language:HTML6.8k19901.9k

• bentrevett/pytorch-seq2seq

  Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

  Language:Jupyter Notebook5.4k651921.3k

• tkipf/pygcn

  Graph Convolutional Networks in PyTorch

  Language:Python5.2k55701.2k

• zhoubolei/introRL

  Intro to Reinforcement Learning (强化学习纲要）

  3.2k928486

• TheNetAdmin/zjuthesis

  Zhejiang University Graduation Thesis LaTeX Template

  Language:TeX2.6k15312619

• eclipse-sumo/sumo

  Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.

  Language:Python2.5k6915k1.4k

• qingsongedu/time-series-transformers-review

  A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

  2.5k601241

• ddz16/TSFpaper

  This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

  2k6523178

• yuqinie98/PatchTST

  An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

  Language:Python1.6k14115271

• KimMeen/Time-LLM

  [ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

  Language:Python1.4k16150244

• MemorialCheng/deep-learning-from-scratch

  《深度学习入门-基于Python的理论与实现》，包含源代码和高清PDF(带书签)；慕课网imooc《深度学习之神经网络(CNN-RNN-GAN)算法原理-实战》；《菜菜的机器学习sklearn》

  Language:Jupyter Notebook1.2k152357

• qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM

  A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.

  91429366

• omerbsezer/LSTM_RNN_Tutorials_with_Demo

  LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

  Language:Python708193296

• DAMO-DI-ML/NeurIPS2023-One-Fits-All

  The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"

  Language:Python46954565

• Fankouzu/smart-contract

  学习智能合约

  Language:Solidity455162273

• thuml/Corrformer

  About code release of "Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model", Nature Machine Intelligence, 2023. https://www.nature.com/articles/s42256-023-00667-9

  Language:Python15981124

• cczu-osa/aki

  奶茶机器人核心

  Language:Python8742120

• xumwen/spatial-temporal-forecast

  Language:Python8435

• kaitokuroba7/ZJU-Clock-In

  浙江大学健康打卡

  Language:Python1000