啟思博學習深度學習
Teacher: 朱學亭
2022/10/28~2023/01/18
Registration deadline:2023/01/10
8Hours/10Weeks (Current Running)

Abstract

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.

Course Objective

1.認知面:本課程教導學生理解深度學習基本原理。

2.技能面:學生可以用深度學習來解決實際的資料問題。

3.情意面:用深度學習研究最新的人工智慧應用。

 Instructor

朱學亭老師

教師簡介

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.

Course Schedule

Unit 1:Introduction to Deep Learning

Unit 2:Numpy quick tutorial

Unit 3:Image and Vision basics

Unit 4:TensorFlow tutorial

Unit 5:Midterm

Unit 6:PyTorch tutorial

Unit 7:Introduction to AOI

Unit 8:Introduction to Object detection

Unit 9:Introductio to NLP(Natural Language Processing)

Unit 10:Final exam

Course Contents

(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.

Grading Policy

平時測驗:佔總成績  30%

期中考:佔總成績  30%

期末考:佔總成績  40%

Passing Criteria


Course Passing Grade:60 Full Score 100 point

Prerequisites

具備基本網頁流覽及圖片格式之相關知識即可,適合所有對AI有興趣的學習者修習。

Certificate

本課程證書費用:250元

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