Build your own deep learner

• Main Author: Wong, David, M. Eng. (David Y.). Massachusetts Institute of Technology
• Other Authors: Kalyan Veeramachaneni.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/113452

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.