摘要
Deep learning is currently the main technology used in artificial intelligence. Most of the deep learning textbooks focus on the principles and mathematical derivation of deep learning models. Few textbooks clearly explain the input and output of deep learning models, making it difficult for students to write programs for using deep learning models.
Therefore, the Kissipo learning emphasizes using the IPO model to divide the deep learning program into three parts: Input, Process and Output.
It enables students to understand how various kinds of big data are processed to train deep learning models, and how the data is transformed inside and outside the model, and finally the prediction results can be output.
#人工智能 #英语授课 #EMI全英授课 English-Medium Instruction
Therefore, the Kissipo learning emphasizes using the IPO model to divide the deep learning program into three parts: Input, Process and Output.
It enables students to understand how various kinds of big data are processed to train deep learning models, and how the data is transformed inside and outside the model, and finally the prediction results can be output.
#人工智能 #英语授课 #EMI全英授课 English-Medium Instruction
课程目标
1.认知面：本课程教导学生理解深度学习基本原理。
2.技能面：学生可以用深度学习来解决实际的资料问题。
3.情意面：用深度学习研究最新的人工智能应用。
授课教师
朱学亭老师
教师简介Prof. Chu is currently an associate professor in the Department of Information Engineering and Ph.D. in Artificial Intelligence at Asia University. He has many years of experience in AI teaching and industry-university cooperation.
His main research fields are Artificial Intelligence, Machine Learning and Deep Learning, Medical informatics and Genomics.
课程进度表
单元 1:Introduction to Deep Learning
单元 2:Numpy quick tutorial
单元 3:Image and Vision basics
单元 4:TensorFlow tutorial
单元 5:Midterm
单元 6:PyTorch tutorial
单元 7:Introduction to AOI
单元 8:Introduction to Object detection
单元 9:Introductio to NLP(Natural Language Processing)
单元 10:Final exam
单元 2:Numpy quick tutorial
单元 3:Image and Vision basics
单元 4:TensorFlow tutorial
单元 5:Midterm
单元 6:PyTorch tutorial
单元 7:Introduction to AOI
单元 8:Introduction to Object detection
单元 9:Introductio to NLP(Natural Language Processing)
单元 10:Final exam
课程内容
(1) Basic Feed-forward to CNN/RNN and other neural network architectures, as well as the use of different neural network layers.
(2) The calculation and principles of loss function, optimization function, back-propagation algorithm, etc.
(3) Deep learning methods for image and video processing.
(4) Deep learning methods for natural language processing.
(5) The input and output formats of the deep learning models.
评分标准
平时测验 Practice：佔总成绩 30%
期中考 Midterm：佔总成绩30%
期末考 Final Exam：佔总成绩 40%
通过标准
课程及格标准:60分Grade Memo:max grade 100 point
先修科目或先备能力
具备基本网页流览及图片格式之相关知识即可，适合所有对AI有兴趣的学习者修习。
