Date of Award
January 2016
Degree Type
Thesis
Degree Name
Master of Science in Electrical and Computer Engineering (MSECE)
Department
Electrical and Computer Engineering
First Advisor
Eugenio Culurciello
Committee Member 1
Anand Raghunathan
Committee Member 2
Vijay Raghunathan
Abstract
Convolutional Neural Networks (CNNs) are biologically inspired feed forward artificial neural networks. The artificial neurons in CNNs are connected in a manner similar to the neurons in the mammalian visual system. CNNs are currently used for image recognition, semantic segmentation, natural language processing, playing video games and many other applications. A CNN can consist of millions of neurons that require billions of computations to produce a single output.
Recommended Citation
Zaidy, Aliasger, "ACCURACY AND PERFORMANCE IMPROVEMENTS IN CUSTOM CNN ARCHITECTURES" (2016). Open Access Theses. 1197.
https://docs.lib.purdue.edu/open_access_theses/1197
