jiuyue's starred repositories
annotated_deep_learning_paper_implementations
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
learnGitBranching
An interactive git visualization and tutorial. Aspiring students of git can use this app to educate and challenge themselves towards mastery of git!
paper-reading
深度学习经典、新论文逐段精读
deeplearning-models
A collection of various deep learning architectures, models, and tips
vim-galore-zh_cn
Vim 从入门到精通
echarts-for-weixin
基于 Apache ECharts 的微信小程序图表库
one-python-craftsman
来自一位 Pythonista 的编程经验分享,内容涵盖编码技巧、最佳实践与思维模式等方面。
PaddleRec
Recommendation Algorithm大规模推荐算法库,包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM,DSIN,SIGN,IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM,TiSAS,AutoFIS等,包含经典推荐系统数据集criteo 、movielens等
lite.ai.toolkit
🛠 A lite C++ toolkit of awesome AI models, support ONNXRuntime, MNN. Contains YOLOv5, YOLOv6, YOLOX, YOLOR, FaceDet, HeadSeg, HeadPose, Matting etc. Engine: ONNXRuntime, MNN.
AI-RecommenderSystem
该仓库尝试整理推荐系统领域的一些经典算法模型
RecSysPapers
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
MiniTV-for-couples
基于ESP32的情侣小电视
wxcloudrun-django
微信云托管 django 框架模版
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.