Popular GitHub Repositories for Truncated Lasso

H2Oai GPU Edition

Sparse and structured neural attention mechanisms

Machine learning algorithms in Dart programming language

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

Baseline implementation of recurrent PPO using truncated BPTT

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"

GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

RL-Toolkit: A Research Framework for Robotics

This repository is dedicated to the machine learning analysis on StockX prices. Blog ?

? Predict car prices instantly with Linear & Lasso Regression! Built with Streamlit, scikit-learn, pandas & matplotlib. Compare models, explore data, and learn ML hands-on. Fast, open source, and easy to use for students & developers!

In this project I have performed analysis and prediction on 1,3,and 5 year returns on 1064 mutual funds in India. I have scraped data from a website which is the most visited website for mutual fund investments.I have tested regression models linear model,SGD Regressor , Random Forest Regressor,Decision Tree Regressor,Ridge,MLP Regressor and linear model (Lasso).After which I have selected the best perorming model and performed Hyper parameter tuning and then deployed an interactive application which can generate the visualization and send an email with the visualization to the users email address.

A Machine Learning project for Machine Learning Internship offered by InternshipStudio.

Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net

Official implementation of "Diffusion-Inspired Truncated Sampler for Text-Video Retrieval (NeurIPS 2024)"

A Python library for sGMRFmix model for anomaly detection in time series

Fast Sparse Linear Models for Big Data with SAGA

End-to-end machine learning project for diamond price prediction using Lasso Regression with 93% accuracy.

The project aims to design machine learning algorithm which is able to predict energy densities of supercapacitors by using input data that is enriched from the results came from image processing techniques.