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algorithm-primary

算法和数据结构新手班

algorithmbasic2020

算法和数据结构体系学习班

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

class-notes

全部算法课讲解知识点提纲、题目汇总、PPT合集、学前必读、学习规划

coder2gwy

互联网首份程序员考公指南，由3位已经进入体制内的前大厂程序员联合献上。

coding-for-great-offer

大厂算法和数据结构刷题班

cqwm

苍穹外卖，已完成全功能，适用于毕设等。。

deeplearningexamples

深度学习中的一些小Demo。

fabric-course

教学

first-project

这是一个测试，新上手用的

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

leetcode-top-interview-questions

lstm-load-forecasting

Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.

lstm-multistep-forecasting

Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-scrolling forecasting, and seq2seq forecasting.

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

ltsf-linear

This is the official implementation for AAAI-23 Oral paper "Are Transformers Effective for Time Series Forecasting?"

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

nab

The Numenta Anomaly Benchmark

nowcoder-community

基于SpringBoot仿牛客网讨论社区

publicarena

Public Arena dataset

pytorch-lstm-forecast

基于pytorch搭建多特征LSTM时间序列预测

pytorch_tutorial

《Pytorch模型训练实用教程》中配套代码

skytakeout

苍穹外卖点餐平台，已完成所有功能，适配大多数毕设管理系统，可自定义。

skytakeoutnew

javaweb项目学习

variational-auto-encoder

wftnet

An official implementation of "WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting" (Accepted to ICASSP 2024)