Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75377
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dc.contributor.authorChen, Benedict Si-Yuan-
dc.date.accessioned2018-05-31T02:48:07Z-
dc.date.available2018-05-31T02:48:07Z-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/10356/75377-
dc.description.abstractDeep learning frameworks such as Caffe and Tensorflow has significantly eased the process of creating deep neural networks. However, these frameworks do not come with a built in visual interface which can be difficult to use. NVIDIA Deep Learning GPU Training System (DIGITS) bridges the gap by providing a Graphic User Interface (GUI) for using deep learning frameworks. This report presents the process behind building a prototype based on NVIDIA DIGITS for a new framework, PyTorch. This prototype aims to have basic functionalities to create a dataset and to train a neural network model. The report covers three main sections. First, the NVIDIA DIGITS section will cover the basic features and information on using the interface. Secondly, the Materials and Methods section will cover the hardware and software used in the project and the brief outline of the procedures taken to develop the prototype. Thirdly, the Results and Discussion section covers the results from the prototype and discussions on how it can be further improved towards full integration of the PyTorch framework.en_US
dc.format.extent67 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleDevelopment of prototype based on NVIDIA DIGITS for PyTorchen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorTan Yap Pengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.organizationNVIDIAen_US
dc.contributor.supervisor2Yin Jian Xiongen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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