Overview

Deep learning is a core technology in AI, but most textbooks focus heavily on theoretical principles and mathematical derivations. This course, Kissipo Learning, simplifies deep learning programming by using the IPO Model (Input, Process, Output), helping students understand how deep learning processes large datasets, transforms data, and generates predictions.

🔹 Hashtags: #AI #DeepLearning #EMI #English-MediumInstruction

Course Objectives

1️⃣ Cognitive: Understand deep learning principles.
2️⃣ Skills: Apply deep learning to real-world data problems.
3️⃣ Affective: Explore the latest AI applications using deep learning.

Instructor

👨‍🏫 Prof. Xue-Ting Chu

• Associate Professor, Department of Information Engineering, Asia University.
• Ph.D. in AI with extensive industry-university collaboration experience.
• Research areas: AI, Machine Learning, Deep Learning, Medical Informatics, Genomics.

Course Syllabus

📌 Unit 1: Introduction to Deep Learning
📌 Unit 2: Numpy Quick Tutorial
📌 Unit 3: Image and Vision Basics
📌 Unit 4: TensorFlow Tutorial
📌 Unit 5: Midterm Exam
📌 Unit 6: PyTorch Tutorial
📌 Unit 7: Introduction to AOI (Automated Optical Inspection)
📌 Unit 8: Introduction to Object Detection
📌 Unit 9: Introduction to NLP (Natural Language Processing)
📌 Unit 10: Final Exam

Course Content

🔹 Neural network architectures: Feed-forward, CNN, RNN
🔹 Loss function, optimization, back-propagation
🔹 Deep learning for image & video processing
🔹 NLP applications
🔹 Deep learning model input/output formats

Grading

✅ Practice Tests: 30%
✅ Midterm Exam: 30%
✅ Final Exam: 40%
🔹 Passing Grade: 60/100

Prerequisites

🔹 Basic knowledge of web browsing & image formats.
🔹 Open to all learners interested in AI.

📢 Don't forget to engage in discussions and complete quizzes! Happy learning! 🚀