2019 Open source projects that are alternatives of or similar to Nni

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

AutoGluon: AutoML for Text, Image, and Tabular Data

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL [email protected]

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

[UNMAINTAINED] Automated machine learning for analytics & production

a distributed Hyperband implementation on Steroids

Sequential Model-based Algorithm Configuration

An open source python library for automated feature engineering

Automated Machine Learning with scikit-learn

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

An automatic ML model optimization tool.

Library for Semi-Automated Data Science

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

LAMA - automatic model creation framework

[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

MLBox is a powerful Automated Machine Learning python library.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Data transformations for the ML era

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

PaddleSlim is an open-source library for deep model compression and architecture search.

Features selector based on the self selected-algorithm, loss function and validation method

Awesome Neural Architecture Search Papers

An intuitive library to extract features from time series

A fast and lightweight AutoML library.

State-of-the art Automated Machine Learning python library for Tabular Data

"NAS evaluation is frustratingly hard", ICLR2020

EvalML is an AutoML library written in python.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

a delightful machine learning tool that allows you to train, test, and use models without writing code

Stock for Deep Learning and Machine Learning

Learnable Embedding Space for Efficient Neural Architecture Compression

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

PHP-ML - Machine Learning library for PHP

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Automated modeling and machine learning framework FEDOT

Official Implementation of 'Fast AutoAugment' in PyTorch.

This repo is about NAS

AutoML library for deep learning

Codebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.

Encodings for neural architecture search

A simplified version of featuretools for Spark

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

DeltaPy - Tabular Data Augmentation (by @firmai)

Automated deep learning algorithms implemented in PyTorch.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

An open source python library for automated feature engineering based on Genetic Programming

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Efficient Learning of Augmentation Policy Schedules

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.