Review of Deep Learning
This is an incremental review paper that synthesizes existing knowledge for researchers and practitioners in artificial intelligence.
This paper reviews the current state of deep learning, outlining basic models like multilayer perceptrons, CNNs, and RNNs, analyzing emerging variants, and summarizing applications in speech processing, computer vision, and natural language processing, while discussing existing problems and possible solutions.
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.