Deep learning has exploded in prominence in scientific computing, with its techniques being utilized by a wide range of sectors to solve complicated issues. To perform certain tasks, all deep learning algorithms employ various forms of neural networks. This article looks at how deep learning algorithms function to replicate the human brain and how important artificial neural networks are. Deep learning is a branch of machine learning that aims to get closer to artificial intelligence's core goal. The summary and induction methods of deep learning are mostly used in this study. It begins with an overview of global progress and the current state of deep learning. Second, it discusses the structural principle, characteristics, and several types of traditional deep learning models, including the stacked autoencoder, deep belief network, deep Boltzmann machine, and convolutional neural network. Third, it covers the most recent advances and applications of deep learning in a variety of disciplines, including speech recognition, computer vision, natural language processing, and medical applications. Finally, it discusses deep learning's challenges and potential research areas.