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Starred repositories
Ongoing research training transformer models at scale
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、ESM…
RSTutorials: A Curated List of Must-read Papers on Recommender System.
推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/
OpenMMLab Rotated Object Detection Toolbox and Benchmark
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
Pytorch implementation for Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition
Reading list for research topics in multimodal machine learning
前沿论文持续更新--视频时刻定位 or 时域语言定位 or 视频片段检索。
A curated list of deep learning resources for video-text retrieval.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
An official implementation for " UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation"
Tensors and Dynamic neural networks in Python with strong GPU acceleration
All Algorithms implemented in Python
Code for ALBEF: a new vision-language pre-training method
A web-based collaborative LaTeX editor
A curated list of Multimodal Related Research.
主要是我是日常看过的不错的文章的资源汇总，方便自己也分享给大家。有些我看过的，就会做简单的解读，没看过的，就先罗列一下，然后之后看了把解读更新上；涉及到搜索/推荐/自然语言处理。
scikit-learn: machine learning in Python
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".
Model summary in PyTorch similar to `model.summary()` in Keras
The implementation of focal loss proposed on "Focal Loss for Dense Object Detection" by KM He and support for multi-label dataset.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.