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

An autoML framework & toolkit for machine learning on graphs.

DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images

Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.

Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades

Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt

Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms

A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle

Grammaropt : a framework for optimizing over domain specific languages (DSLs)

Pipelineopt, sckit-learn automatic pipeline optimization

Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades

Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)