Repository Index

A hyperparameter optimization framework

• python
• machine-learning
• parallel
• distributed

AutoML library for deep learning

• automl
• neural-architecture-search
• automated-machine-learning
• python

Distributed Asynchronous Hyperparameter Optimization in Python

• hacktoberfest

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.

• h2o
• machine-learning
• data-science
• deep-learning

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

• deep-learning
• machine-learning
• ml-platform
• ml-infrastructure

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• deep-learning

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

• pipeline
• pipeline-framework
• machine-learning
• deep-learning

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

• python
• pytorch
• machine-learning
• deep-learning

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.

• svm
• regression
• classification
• machine-learning