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AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
A Python scikit for building and analyzing recommender systems
LAPACK development repository
👑 Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
Four word embedding models implemented in Python. Supporting arbitrary context features
Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang
Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username
模型压缩的小白入门教程，PDF下载地址 https://github.com/datawhalechina/awesome-compression/releases
stable-video-diffusion-webui, img to videos| 图片生成视频
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate recommendations🤩
Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022
Fast truncated singular value decompositions
[ICML2025] Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.
Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their interaction history, similar users, and also the popularity of products.
Randomized Dimension Reduction Library
Digital Image Watermarking Method Based on Hybrid DWT-HD-SVD Technique: Attacks, PSNR, SSIM, NC