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.
Degree Type
Thesis
Degree Name
Master of Science in Electrical and Computer Engineering (MSECE)
Department
Electrical and Computer Engineering
Date of Award
January 2016
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
First Advisor
Eugenio Culurciello
Committee Member 1
Anand Raghunathan
Committee Member 2
Vijay Raghunathan