A Comprehensive Review of Deep Learning Architectures: Applications, Advancements, and Challenges

Abstract

 Machines can now understand intricate patterns from data thanks to deep  learning architectures, which have sparked a revolution in many industries.  Deep learning architectures are the focus of this in-depth analysis of their  uses, developments, and difficulties. Neural networks, activation functions,  and other basic ideas are introduced in the first section of the article.  Subsequently, it delves into major architectures such as “Convolutional Neural  Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial  Networks (GANs), and Transformer models, highlighting their applications in  computer vision, natural language processing, healthcare, and autonomous  vehicles. Recent advancements in deep learning, including novel architectures  and pre-trained models, are discussed, along with their impact on various  domains. Despite these advancements, challenges such as computational  complexity, overfitting, and ethical considerations persist. The paper identifies  unresolved research problems and provides insights into future directions,  emphasizing the need for interpretable and robust deep learning models. Through  this comprehensive examination, the paper contributes to a better understanding  of deep learning architectures and their implications for AI research and  applications.