71 Open source projects that are alternatives of or similar to L0Learn

Statistical Models with Regularization in Pure Julia

Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models

All Relevant Feature Selection

Feature Selection for Clustering

Python utilities for Machine Learning competitions

Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis

A Deep Learning Approach to Ultrasound Image Recovery

Exemplary, annotated machine learning pipeline for any tabular data problem.

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Search the best feature subset for you classification mode

Sparse modeling and Compressive sensing in Python

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

A FEAture Selection Toolbox for C/C+, Java, and Matlab/Octave.

A package to integrate rollout into your Laravel project.

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Iterative unfolding for Python

Research project

Code for Learned Turbo-ype Affine Rank Minimization

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Scalable sparse Bayesian learning for large CS recovery problems

Feature engineering package with sklearn like functionality

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

speechpro.com/

Python implementation of QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection problem.

Julia package of regularization functions for machine learning

PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features

A package for AFM image reconstruction and compressed sensing in general

Python/R library for feature selection in neural nets. ("Feature selection using Stochastic Gates", ICML 2020)

Compressed Sensing and Motion Correction LAB: An MR acquisition and reconstruction system

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

Codes and Datasets for paper RecSys'20 "SSE-PT: Sequential Recommendation Via Personalized Transformer" and NurIPS'19 "Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers"

Hyperstar: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning.

机器学习-Coursera-吴恩达- python+Matlab代码实现

Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data

🧠👨‍💻Deep Learning Specialization • Lecture Notes • Lab Assignments

This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.

GraphOfDocs: Representing multiple documents as a single graph

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

Task-Aware Compressed Sensing Using Generative Adversarial Networks (published in AAAI18)

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Feature selection for maximizing expected cumulative reward

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

An un-trained neural network with a potential application in accelerated MRI

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

Ordered Weighted L1 regularization for classification and regression in Python

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

Machine Learning in R

Scaleable input gradient regularization

This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations

Awesome Domain Adaptation Python Toolbox

Statistical Inference and Sure Independence Screening via Ball Statistics

a collection of modern sparse (regularized) linear regression algorithms.

Software for learning sparse Bayesian networks

Sorted L1 Penalized Estimation

code for "A global pathway selection algorithm for the reduction of detailed chemical kinetic mechanisms" (Gao et al., CNF'16)