A Comprehensive Survey of Trending Tools and Techniques in Deep Learning

Abstract

Automated feature learning is now possible in various fields, including healthcare, image recognition, and, more recently, feature extraction and classification of simple and complex human activity detection in mobile and wearable sensors, thanks to advances in deep learning and increased computing capabilities. A significant advancement in artificial intelligence has been made as a result of deep learning and cloud technology integration. As a result of cloud computing, organisations now have access to the necessary resources to develop and implement deep learning solutions. Although it is becoming increasingly common in cloud infrastructures, there is limited research on it. This study aims to provide a comprehensive overview of deep learning and discusses the methodologies, their uniqueness, benefits, and limits. Finally, we define and discuss certain open research difficulties that demand more investigation and improvements.