This paper provides a comprehensive overview of recent advances and future directions in deep learning and neural networks within the domain of computer science engineering. Deep learning has emerged as a transformative technology, enabling remarkable achievements in various fields such as computer vision, natural language processing, and reinforcement learning. This paper delves into key advancements in deep learning architectures, algorithms, and methodologies, highlighting breakthroughs such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Furthermore, it explores cutting-edge research directions, including self-supervised learning, lifelong learning, and neural architecture search, which hold promise for further advancing the capabilities of deep learning models. Through case studies and examples, we illustrate the practical applications of deep learning across diverse domains, from healthcare and autonomous vehicles to finance and entertainment. By elucidating recent trends and future directions, this paper aims to provide insights for researchers, practitioners, and enthusiasts in computer science engineering to navigate the rapidly evolving landscape of deep learning and neural networks.
Volume 16 | Special issue on Trends in Engineering
Pages: 104-112