A Comprehensive Review on Neural Network Architectures
Keywords:
Machine Learning(ML), Deep Learning(DL), Artificial Neural Networks(ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)Abstract
Deep Learning (DL), a vital element of the Fourth Industrial Revolution (4IR) or Industry 4.0 takes a leading position in the realms of machine learning (ML) and artificial intelligence (AI). Rooted in the foundation of artificial neural networks (ANN), DL technology has emerged as a pivotal force in contemporary computing. Its capacity to learn from data has rendered it a prominent subject of discussion, finding extensive applications across diverse sectors such as healthcare, visual recognition, text analytics, cybersecurity, and beyond. Nevertheless, crafting a suitable DL model presents a formidable challengeThe challenge stems from the dynamic nature and intrinsic variations present in real-world problems and datasets. Adapting DL to address the intricacies of these challenges requires careful consideration and innovative approaches. As DL continues to shape the technological landscape, its ability to transform industries and address complex problems is becoming increasingly evident. In this paper, we explore into the architecture and feature of Artificial Neural Network(ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Top of Form
