Abstract
Deep learning stands out as a leading technology within the realm of machine learning, leveraging algorithms designed to mimic the operations of the human brain through artificial neural networks. These neural networks serve as the cornerstone of deep learning and offer two key advantages over alternative algorithmic structures. Firstly, their architecture, characterized by stacked non-linear functions, endows them with immense computational power. In this chapter, the authors delve into the exploration of various convolutional neural network models documented in literature, examining their parameters, architectures, layers, as well as their respective strengths and limitations. This exploration is particularly relevant in the context of Web 3.0, where advancements in deep learning play a crucial role in enhancing the capabilities of intelligent web applications and services. The aim is to furnish researchers with a succinct overview of deep learning methodologies and advancements, with a focus on their implications for the evolving landscape of Web 3.0 technologies.