Artificial Neural Network Hyperparameters Optimization: A Survey

Abstract

Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most essential ways to ML; the most challenging element of designing a NN is determining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid researchers and data analysts in developing better ML models via identifying the appropriate hyperparameter configurations