Review of Deep Learning

TL;DR

This paper reviews the recent progress, models, applications, and challenges of deep learning, highlighting its significance in artificial intelligence and outlining future research directions.

Contribution

It provides a comprehensive summary of deep learning models, applications, and challenges, offering insights into emerging trends and potential solutions.

Findings

Deep learning models include multilayer perceptrons, CNNs, and RNNs.

Applications span speech, vision, and natural language processing.

Identifies current problems and suggests future research directions.

Abstract

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.