Build your own deep learner
Name
1020179444-MIT.pdf
Description
Full printable version
Size
19.54 MB
Format
Adobe PDF
Checksum (MD5)
26f1178b848c5502ed0bbd4843f76cbd
Author(s)
Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology
Advisor(s)
Kalyan Veeramachaneni.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
BYODL is a framework for building deep learning-based mobile apps to solve domain-specific image recognition problems. Domain-specific image recognition problems are challenging due to lack of labeled data - few have the expertise to assign labels to the images. By using the mobile app to collect data, our framework speeds up the process of improving the model's performance and makes the updated version readily available to app users. By handling the details of setting up the infrastructure and the mobile app boilerplate, BYODL helps users produce a functional image recognition app in a matter of hours instead of months. We designed BYODL with an eye towards customizability, simplicity, and efficiency, which led to interesting implementation challenges and design trade-offs. In this thesis, we present the motivations for BYODL, discuss aspects of its design and implementation, and report on its use cases in the real world.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-64).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
