Deep Learning Architectures - Advances and Applications: Analyzing recent advances and applications of deep learning architectures in various domains such as computer vision and NLP

Abstract

Deep learning has revolutionized various fields such as computer vision, natural language processing (NLP), and speech recognition, achieving remarkable performance in complex tasks. This paper provides a comprehensive review of recent advances in deep learning architectures and their applications across different domains. We analyze key developments in architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, highlighting their impact on tasks like image classification, object detection, language translation, and sentiment analysis. Additionally, we discuss challenges and future directions for deep learning research, emphasizing the need for interpretable and efficient models. Overall, this paper aims to provide insights into the state-of-the-art in deep learning architectures and their practical implications.