jiuyue's repositories
PaddleRec
Recommendation Algorithm大规模推荐算法库
IURO
This is the source codes of Recsys 2023 paper "Interpretable User Retention Modeling in Recommendation"
EasyRec
A framework for large scale recommendation algorithms.
deeplearning-models
A collection of various deep learning architectures, models, and tips
mitmproxy
An interactive TLS-capable intercepting HTTP proxy for penetration testers and software developers.
AlainConnor
Config files for my GitHub profile.
toyML
Toy Machine Learning Package
DenoisingDiffusionProbabilityModel-ddpm-
This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.
nps
一款轻量级、高性能、功能强大的内网穿透代理服务器。支持tcp、udp、socks5、http等几乎所有流量转发，可用来访问内网网站、本地支付接口调试、ssh访问、远程桌面，内网dns解析、内网socks5代理等等……，并带有功能强大的web管理端。a lightweight, high-performance, powerful intranet penetration proxy server, with a powerful web management terminal.
resume
An elegant \LaTeX\ résumé template. 大陆镜像 https://gods.coding.net/p/resume/git
HowToCook
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Chinese).
lite.ai.toolkit
🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, YOLOv5, YOLOR, NanoDet, YOLOX, SCRFD, YOLOX . MNN, NCNN, TNN, ONNXRuntime, CPU/GPU.
.tmux
🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️
zsh-syntax-highlighting
Fish shell like syntax highlighting for Zsh.
PythonPractice
来源于慕课网《python高效编程实战》
AI-RecommenderSystem
该仓库尝试整理推荐系统领域的一些经典算法模型
vim-galore-zh_cn
Vim 从入门到精通
GNNPapers
Must-read papers on graph neural networks (GNN)
git-commit-emoji-cn
😁 git commit message emoji 使用指南
vim-init
轻量级 Vim 配置框架，全中文注释
transformer
Implementation of "Attention Is All You Need" using pytorch
Depression-Detection-Through-Multi-Modal-Data
Conventionally depression detection was done through extensive clinical interviews, wherein the subject’s re- sponses are studied by the psychologist to determine his/her mental state. In our model, we try to imbibe this approach by fusing the 3 modalities i.e. word context, audio, and video and predict an output regarding the mental health of the patient. The output is divided into a binary yes/no denoting whether the patient has symptoms of depression. We’ve built a deep learning model that fuses these 3 modalities, assigning them appropriate weights, and thus gives an output.
test
Page
no-style-please
A (nearly) no-CSS, fast, minimalist Jekyll theme.
AdversarialNetsPapers
Awesome paper list with code about generative adversarial nets
Reflection_Summary
算法理论基础知识应知应会