Description
Because of its revolutionary capacity to learn from massive quantities of data and uncover complicated patterns, Deep Learning (DL) has been at the forefront of artificial intelligence research and implementation. In this extensive survey, we examine the most pressing developments in DL over the last several years. This work gives a thorough examination of the current status and potential future directions of DL, including developments in model architectures, optimization approaches, interpretability, transfer learning, reinforcement learning, unsupervised learning, generative models, and ethical issues. Our goal is to present a holistic view of the ever-changing environment of DL technologies and their potential influence on society, with an eye toward both academic research and real-world applications.