1 / 14

Artificial Neural Networks

Artificial Neural Networks. Instructor : Prof. Chen Sei-Wang ( 陳世旺 ) Office: Applied Science Building, Room 101 Communication: Tel : 7734-6661 E-mail : schen@csie.ntnu.edu.tw Class Hr. : Wed. 9 :10AM -- 10:30AM

joym
Télécharger la présentation

Artificial Neural Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artificial Neural Networks Instructor : Prof. Chen Sei-Wang (陳世旺) Office: Applied Science Building, Room 101 Communication: Tel: 7734-6661 E-mail:schen@csie.ntnu.edu.tw Class Hr. : Wed. 9:10AM -- 10:30AM 10:50AM – 12:00 NOON Classroom: B102 Office Hr. : Mon. 2:00PM - 4:00PM

  2. Teaching assistant : 林筱芸 Office : Room208 Telephone : 6676 E-mail : hsiaoyun0@gmail.com Office Hrs. : Mon. 10:00 am – 17:00 pm 2

  3. Goal of Course Realizes the early study on Artificial Neural Networks (ANN), which somehow provides theoretical bases for developing computational techniques for Deep Neural Networks (DNN).

  4. (1) Neural Networks: Algorithms, Applications, and Programming Techniques, by James A. Freeman and David M. Skapura, 1992.(2) Neural Networks and Learning Machines3rd Ed., by Simon Haykin, 2009. ( 滄海圖書, 陳式政, 0938723438, (02)27360282) Textbooks:

  5. Syllabus I Ch1: Introduction (第1,2週) Ch2: Adaline and Madaline (第2,3週) Ch3: Model Building through Regression (第4週) Ch4: Backpropagation (第5,6週) Ch5: Convolutional Neural Networks (第7,8週) Ch6: AM and BAM (第9,10週) Ch7: Hopfield Neural Model (第11,12週) Ch8: Counterpropagation Neural Model (第13,14週) 5

  6. Ch9: Self-Organizing Feature Maps (第15週) Ch10: Adaptive Resonance Theory (第16,17,18週) Ch11: Spatiotemporal Pattern Classification (第19,20週) Ch12: Boltzmann Machine (第21,22週) Ch13: Deep Neural Networks (第23--週) Deep belief networks, Autoencoders, Restricted Boltzmann machines, …….. 6

  7. Syllabus II Ch.1: Introduction (2/27) Ch.2: Adaline and Madaline (2/27, 3/6) Ch.4: Backpropagation Neural Networks (3/6, 3/13) Ch.12:Boltzmann Machine (3/20, 3/27) Mid-term Exam. (4/10) 7

  8. Steps for finding the power points of Chen’s chapters: • 資工系網頁: http://www.csie.ntnu.edu.tw/ • 系所成員 • 名譽教授 • 網站:http://www.csie.ntnu.edu.tw/~ipcv • Prof. Sei-Wang Chen (陳世旺教授) • Teaching • Artificial Neural Networks 8

  9. References: • Books (1) Neural and Adaptive Systems: Fundamentals Through Simulations, by Jose C. Principe, Neil R, Euliano, and W. Curt Lefebvre, 2000. (2) Neural Networks and Intellect Using Model-Based Concepts, by Leonid I. Perlovsky, 2001. (3) Neural Networks - A Classroom Approach, 2nd Ed., by Satish Kumar, 2013. (4) Deep Learning, by I. Goodfellow and Y. Bengio and A. Courville, The MIT Press, 2016. (5) Neural Networks and Deep Learning, by Michael Nielsen, 2017.

  10. (B) Journals (1) IEEE Trans. on Neural Networks (2) IEEE Trans. on Parallel and Distributed Systems (3) Neural Networks (4) Neurocomputing (5) Cognitive Science (C) Conferences (1) Int’l Conference on Neural Networks

  11. Assignment Submission • Prepare the assignment file File Name: NAME.HW# , e.g., 陳世旺.HW1 Content: (i) Problem statement (ii) (a) General assignment – Answer (b) Program assignment – (1) Input/Output (2) Source code (3) Comments 11

  12. (B) Submit the file to: (1) Moodle https://moodle.ntnu.edu.tw/ (2)登入

  13. (3) 點選課程 (4) 點選所要交之作業區 ex. Homework1 (5) 上傳作業 作業繳交時間在下一次上課前

  14. Evaluation Interaction 30% Homework 40% Midterm 30% Late homeworks will not be accepted Plagiarism is definitely not allowed 14

More Related