关键词: 深度学习, 卷积神经网络, 深度置信网络, 自编码器, 循环神经网络, 深度堆叠网络

Abstract: Deep learning is a broader class of machine learning method based on data representation. Its emergence has not only promoted the development of machine learning, but also accelerated the innovation of artificial intelligence. This paper discusses and compares several typical models of deep learning. It first investigates restricted Boltzmann machine, deep belief network and auto-encoder, and explores their structure, principle, advantages and disadvantages of these unsupervised learning models in detail. Secondly, it discusses several supervised learning models including convolutional neural network, recurrent neural network and deep stacked network. And it also evaluates and analyzes the model structure and working principle. Then it makes a contrastive analysis of typical deep learning models and performs comparative experiments. Deep belief network and convolutional neural network are applied to the handwriting digits recognition task and experimental results show that deep learning models have better recognition performance than traditional neural network. Finally, it discusses the developments and challenges of deep learning in the future.

Key words: deep learning, convolutional neural network, deep belief network, auto-encoder, recurrent neural network, deep stacked network