Svd Python Packages

scikit-surprise

A Python scikit for building and analyzing recommender systems

cherab-inversion

CHERAB for inversion tools

neural-memory-mcp

Knowledge graph plugin for Claude Code - map your codebase into layered, navigable neural nodes

crikit2

Hyperspectral imaging (HSI) processing and analysis platform (user interface, UI)

pyreclab

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.

rho-eval

Behavioral auditing toolkit for LLMs — audit any model across 8 dimensions (factual, toxicity, bias, sycophancy, reasoning, refusal, deception, over-refusal) using teacher-forced confidence probes.

knowledge-fidelity

Compress LLMs while auditing whether they still know truth vs myths. SVD compression + false-belief detection in one toolkit.

scikit-surprise-2

An easy-to-use library for recommender systems.

diffred

[AISTATS 2024] DiffRed: Dimensionality Reduction guided by stable rank

ristretto

ristretto: Randomized Dimension Reduction Library

simpliml

Machine Learning, Artificial Intelligence, Mathematics

delayedsparse

Efficient sparse matrix implementation for various "Principal Component Analysis"

regml

Regularization for Machine Learning-RegML GUI

matrix-fact

Python Matrix Factorization Module

spectral-kv

Up to 28x KV cache compression for LLMs via spectral SVD projection. Practically lossless on modern architectures.

tfrec

A recommender library built on top of Tensorflow and Keras with implementations of SVD and SVD++ algorithms.

sorsa

SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models

tfcf

tf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.