zbkhzd2005's Stars
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
ChatGPTNextWeb/ChatGPT-Next-Web
A cross-platform ChatGPT/Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT/Gemini 应用。
openai/openai-cookbook
Examples and guides for using the OpenAI API
microsoft/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
openai/openai-python
The official Python library for the OpenAI API
infiniflow/ragflow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
atherosai/ui
Simple UI examples from my social media
garrettj403/SciencePlots
Matplotlib styles for scientific plotting
tensorchord/Awesome-LLMOps
An awesome & curated list of best LLMOps tools for developers
openai/glide-text2im
GLIDE: a diffusion-based text-conditional image synthesis model
infiniflow/infinity
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
Nixtla/nixtla
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
kiukotsu/ucore
清华大学操作系统课程实验 (OS Kernel Labs)
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.
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
kavinwow100/Deeplearning-and-Coding
学习深度学习不如边写代码边学习,实际操作一遍才能理解数据的变换过程,参数的训练过程,这里整合了B站的jupter代码,可以结合着B站的视频边看边练,希望能对大家有帮助。