類神經網路理論與實務
Neural Network Theory and Practice
學期
108-2
學分
3
學分
當期課號
5158
永久課號
ILP5134
開課單位
照明與能源光電研究所
授課教師
林秀菊
校區
歸仁
類別
選修
上課時間表
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 類神經網路理論與實務 CM215 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
概述
The purpose of this course is to help students understand the theory and applications of neural networks. Students will have the ability to solve related problems using neural networks models.
先修科目
線性代數、機率統計、工程數學
教學方式
使用MATLAB模擬
評分方式
到課(10%) Labs(20%)、筆試(35%)與期末專題(35%)
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction; Labs tool introduction |
| 第 2 週 | Neuron Model and Network Architectures An illustrative example |
| 第 3 週 | Perceptron Learning Rule |
| 第 4 週 | Signal and Weight Vector Spaces Linear Transformations for Neural Networks Hands-on Labs using Matlab |
| 第 5 週 | Supervised Heb's Learning |
| 第 6 週 | Performance Surfaces and Optimum Points |
| 第 7 週 | Performance Optimization |
| 第 8 週 | Widrow-Hoff Learning |
| 第 9 週 | Backpropagation Matlab project tracing oral report-1 |
| 第 10 週 | Variations on Back propagation Matlab project tracing oral report-2,3 |
| 第 11 週 | Midterm Pencil Exam |
| 第 12 週 | Midterm Exam discussion (1H) Workspace on workstation (1H) Deep Learning onramp (hands-on 1H ) |
| 第 13 週 | Matlab project tracing oral report-4,5 Variations on Backpropagation |
| 第 14 週 | Variations on Backpropagation Convolution Neural Networks and its Variants Final project proposal hand-in |
| 第 15 週 | Practical Training Issues Project Simulation |
| 第 16 週 | Project Simulation for Transfer learning |
| 第 17 週 | Final Project Demo and oral Report |
| 第 18 週 | Hand in the written final project report |
教科書
Neural Network Design, 2/e Martin T Hagan, Howard B Demuth, Mark H Beale, Orlando De Jesús, 2013 Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville The MIT Press 9780262035613 2016