Document Type
Article
Abstract
Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This tutorial presents an exploration of optimization strategies and hyperparameter tuning techniques for neural networks using state-of- the-art Python libraries.
Publication Info
Preprint version 2023.
© Kaushik Roy, 2023
APA Citation
Roy, K. (2023). Tutorial - Shodhguru Labs: Optimization and Hyperparameter Tuning for Neural Networks. [Preprint]