Popular GitHub Repositories for Semiotic Squares

A library for differentiable nonlinear optimization

End-to-end Lane Detection for Self-Driving Cars (ICCV 2019 Workshop)

Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

Chainer implementation of Least Squares GAN (LSGAN)

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

Mode collapse example of GANs in 2D (PyTorch).

TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.

Simple implementation of Least Squares Generative Adversarial Networks

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

XCSF learning classifier system: rule-based online evolutionary machine learning

GAN that adopts the least squares loss function for the discriminator in tensorflow

Recommender system in retail

Recommendation System using MLlib and ML libraries on Pyspark

Python scripts that implement collaborative filtering using Matrix Factorization with Alternating Least Squares (MF-ALS) for hotels and restaurants, Restricted Boltzmann Machines (RBM) for attractions, and content-based filtering using cosine similarity for the "More Like This" feature.

This repository explores recommendation engines from simple Matrix Factorization methods to NeurMF Methods

A pipeline that collects, preprocesses and labels raw user-item interaction data to build a recsys with collaborative filtering (SVD, ALS) and LTR (XGBoost ranking) methods

达多伊·达德索提出的RJ-PINNs框架是一种开创性方法，它使用基于雅可比矩阵的最小二乘法（TRF）直接最小化残差，无需传统的损失函数。这是首个在PINNs中消除梯度优化器的方案，为反向偏微分方程问题提供了无与伦比的鲁棒性。

Interpolation, linear equations, least squared, eigenvalues, ode, quadratures, roots, minimization, Monte-Carlo, neural networks