Multi-class classification using deeping learning

Nowadays, with the fast development of the big data and artificial intelligent, deep learning plays a significant role in different fields and infrastructures. Deep learning is a new field in machine learning research. The motivation is to build and simulate a neural network for human brain analysis...

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主要作者: Bo, Hu
其他作者: Su Rong
格式: Final Year Project
語言:English
出版: 2019
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在線閱讀:http://hdl.handle.net/10356/78320
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機構: Nanyang Technological University
語言: English
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spelling sg-ntu-dr.10356-783202023-07-07T16:59:38Z Multi-class classification using deeping learning Bo, Hu Su Rong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Nowadays, with the fast development of the big data and artificial intelligent, deep learning plays a significant role in different fields and infrastructures. Deep learning is a new field in machine learning research. The motivation is to build and simulate a neural network for human brain analysis and learning. It mimics the mechanism of the human brain to interpret data such as images, sounds and texts. Multi-class classification algorithms like support vector machine (SVM), and convolutional neural networks (CNNs) using deep learning are used to analyse data for classification and regression analysis. They are commonly implemented in image classification. The aim of this project is to evaluate and compare three commonly used multiclass classification methods in image classification for future application. The project can be divided into three parts. In the first part, the project explains the working principle behind CNN. In the second part, Kaggle database images are used to conduct the experiment in Matlab2019a. The last part evaluates the experiment and discuss the future work and application. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-18T02:20:41Z 2019-06-18T02:20:41Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78320 en Nanyang Technological University 47 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Bo, Hu
Multi-class classification using deeping learning
description Nowadays, with the fast development of the big data and artificial intelligent, deep learning plays a significant role in different fields and infrastructures. Deep learning is a new field in machine learning research. The motivation is to build and simulate a neural network for human brain analysis and learning. It mimics the mechanism of the human brain to interpret data such as images, sounds and texts. Multi-class classification algorithms like support vector machine (SVM), and convolutional neural networks (CNNs) using deep learning are used to analyse data for classification and regression analysis. They are commonly implemented in image classification. The aim of this project is to evaluate and compare three commonly used multiclass classification methods in image classification for future application. The project can be divided into three parts. In the first part, the project explains the working principle behind CNN. In the second part, Kaggle database images are used to conduct the experiment in Matlab2019a. The last part evaluates the experiment and discuss the future work and application.
author2 Su Rong
author_facet Su Rong
Bo, Hu
format Final Year Project
author Bo, Hu
author_sort Bo, Hu
title Multi-class classification using deeping learning
title_short Multi-class classification using deeping learning
title_full Multi-class classification using deeping learning
title_fullStr Multi-class classification using deeping learning
title_full_unstemmed Multi-class classification using deeping learning
title_sort multi-class classification using deeping learning
publishDate 2019
url http://hdl.handle.net/10356/78320
_version_ 1772826912221036544