Build your own deep learner
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/113452 |
| _version_ | 1811085169237426176 |
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| author | Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology |
| author2 | Kalyan Veeramachaneni. |
| author_facet | Kalyan Veeramachaneni. Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology |
| author_sort | Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T13:03:58Z |
| format | Thesis |
| id | mit-1721.1/113452 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:03:58Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1134522019-04-11T09:44:27Z Build your own deep learner Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology Kalyan Veeramachaneni. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by David Wong. M. Eng. 2018-02-08T15:58:18Z 2018-02-08T15:58:18Z 2017 2017 Thesis http://hdl.handle.net/1721.1/113452 1020179444 eng 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. http://dspace.mit.edu/handle/1721.1/7582 64 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology Build your own deep learner |
| title | Build your own deep learner |
| title_full | Build your own deep learner |
| title_fullStr | Build your own deep learner |
| title_full_unstemmed | Build your own deep learner |
| title_short | Build your own deep learner |
| title_sort | build your own deep learner |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/113452 |
| work_keys_str_mv | AT wongdavidmengdavidymassachusettsinstituteoftechnology buildyourowndeeplearner |