深度学习大数据分析中英文外文文献翻译.doc

本科毕业设计（论文） 中英文对照翻译 (此文档为word格式,下载后您可任意修改编辑!) 标题：Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis 作者：Alcides Fonseca, Bruno Cabral 期刊：Big Data Research，卷8：50-56页 年份：2017 原文 Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis Alcides Fonseca, Bruno Cabral Abstract Big Data concerns with large-volume complex growing data. Given the fast development of data storage and network, organizations are collecting large ever-growing datasets that can have useful information. In order to extract information from these datasets within useful time, it is important to use distributed and parallel algorithms. One common usage of big data is machine learning, in which collected data is used to predict future behavior. Deep-Learning using Artificial Neural Networks is one of the popular methods for extracting information from complex datasets. Deep-learning is capable of more creating complex models than traditional probabilistic machine learning techniques. This work presents a step-by-step guide on how to prototype a Deep-Learning application that executes both on GPU and CPU clusters. Python and Redis are the core supporting tools of this guide. This tutorial will allow the reader to understand the basics of building a distributed high performance GPU application in a few hours. Since we do not depend on any deep-learning application or framework—we use low-level building blocks—this tutorial can be adjusted for any other parallel algorithm the reader might want to prototype on Big Data. Finally, we will discuss how to move from a prototype to a fully blown production application. Keywords: Big-data; Deep-learning; Prototyping; GPGPU; Cluster; Parallel programming Introduction Deep Learning refers to the usage of Artificial Neural Networks (ANN or NN) with several hidden layers used for data with a high dimensionality. A common example and benchmark for Deep Learning is image classification from the ImageNet dataset. ANNs can be