计算机应用研究2016，Vol.33Issue(11)：3201-3205,5.DOI:10.3969/j.issn.1001-3695.2016.11.001
深度学习应用技术研究
Study on application technology of deep learning
摘要
Abstract
This paper reviewed the deep learning algorithms and their applications.It elaborated the greedy layer training al-gorithm which used the fine-grained back-propagation (BP)learning following the layer-wise pre-training on each restricted Boltzmann machine (RBM)layer.After comparing and analyzing the three ways of gradient descent in the BP algorithm,this paper suggested applying stochastic gradient descent in online learning and adopting stochastic mini-batch gradient descent in static offline learning.It summarized the characteristic of the network structure in deep learning and recommend the design of state-of-art five-layer network architecture.It also analyzed the necessity of the nonlinear activation function in feedforward neural networks and the advantages of the common activation functions,and recommended using ReLU activate function.Fi-nally,the paper provided a brief summary of features and application scenarios of emerging deep neural networks such as deep CNN(convolutional neural networks),deep RNNs(recurrent neural networks)and LSTM(long short-termmemory networks), as well as the potential directions of future deep learning applications and research.关键词
受限玻尔兹曼机/深度神经网络/梯度下降/验证集/监督学习/贪婪层训练方法/深度学习/深度学习层次结构Key words
RBM/DNN/gradient descent/training set/supervised learning/greedy layer training/deep learning/deep learning network architecture分类
信息技术与安全科学引用本文复制引用
毛勇华,桂小林,李前,贺兴时..深度学习应用技术研究[J].计算机应用研究,2016,33(11):3201-3205,5.基金项目
国家自然科学基金资助项目（61472316，61172090）；国家科技重大专项基金资助项目（2012ZX03002001）；高等教育博士点研究基金资助项目（20120201110013）；陕西省自然科学基金资助项目（2014JM1006，2014KRM28-01）；中央高校基本科研业务费专项资金资助项目（XKJC2014008）；陕西省自然科学创新工程资助项目 （）
