计算机工程与应用2018，Vol.54Issue(10)：1-9,10,10.DOI:10.3778/j.issn.1002-8331.1712-0418
深度学习的研究进展与发展
Research progress and development of deep learning
摘要
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 unsu-pervised learning models in detail.Secondly,it discusses several supervised learning models including convolutional neu-ral 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 net-work.Finally,it discusses the developments and challenges of deep learning in the future.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Key words
deep learning/convolutional neural network/deep belief network/auto-encoder/recurrent neural network/deep stacked network分类
信息技术与安全科学引用本文复制引用
史加荣,马媛媛..深度学习的研究进展与发展[J].计算机工程与应用,2018,54(10):1-9,10,10.基金项目
中国博士后科学基金(No.2017M613087) （No.2017M613087）
国家自然科学基金青年科学基金(No.61403298). （No.61403298）
