Reducing Convergence Time of Data Parallel Approach for Distributed Neural Network

Guguloth, Suresh (2017) Reducing Convergence Time of Data Parallel Approach for Distributed Neural Network. Masters thesis, Indian Institute of Technology Hyderabad.

[img] Text
CS15MTECH11004.pdf - Submitted Version
Restricted to Registered users only until 28 June 2020.

Download (1MB) | Request a copy

Abstract

Data is generated at a ll time with mobile device, sensor, camera, e - commerce sites. Recent work in deep learning and unsupervised feature learning has shown that being able to train large models can dramatically improve performance . deep neural network architectures trained on large data sets can obtain impressive performance across a wide variety of domains like such as object recognition , s peech recognition and image recognition , fraud detection and recommendation systems . While distributed training of neural networks we have some of issue while distributing of data and model in large scale data and model. Those issues are synchronization and communization in distributed system. We are try to design and implement, optimize l arge scale model and data to train in distributed manner.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: distributed deep learning, big data, machine learning, large scale datasets, TD838
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 29 Jun 2017 07:24
Last Modified: 29 Jun 2017 07:24
URI: http://raiith.iith.ac.in/id/eprint/3307
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 3307 Statistics for this ePrint Item