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This repository archives bilingual lecture notes and coursework for a software applications course on applied deep learning, covering neural network basics, CNNs and RNNs, image classification, transfer learning, object detection, sound processing, natural language processing, Transformers, LLMs, and lightweight models.

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[Fall 2025] Software Applications

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This repository archives bilingual lecture notes and coursework for the Software Applications course.

Author: Cheolwon Park (Korea University Sejong, CSE) - Year 2 (Sophomore) as of 2025

📑 Table of Contents



📝 About This Repository

  • Lecture-Notes/ holds lecture notes in the order of the lecture decks (Lec01, Lec02, and so on), and the units that had practice sessions also hold lab notes and example notebooks.
  • Coursework/ holds the explanations of the two paper assignments and a submitted report.
  • Every lecture note, lab note, and assignment explanation comes as an English (.md) and Korean (.ko.md) pair.

🤖 AI Agent Usage Claude Code was used to organize and translate the notes and to restructure the repository.


📚 Course Information

  • Semester: Fall 2025 (September - December)
  • Affiliation: Korea University Sejong
Course Code Course Type Instructor Department
DCCS403-00 SOFTWARE APPLICATIONS Major Elective Prof. Heechan Chae Department of Computer Science and Software Engineering

Course Overview

A course on the data processing techniques and deep learning models used across software application fields such as images, sound, and text, in which each theory lecture is followed by practice in applying them. The instructor, schedule, assessment, course policies, and progress are in the course information document.


✅ Prerequisites

  • Basic Python programming skills

  • Understanding of basic machine learning concepts

  • A Google account for using Google Colab

  • 💻 Development Environment

Tool Company OS Notes
Google Colab Google Web Environment for running the practice notebooks
PyTorch PyTorch Foundation Linux (Colab) Deep learning framework, including torchvision


🗂 Repository Structure

25Fall_Software-Applications
├── Lecture-Notes               Lecture notes, lab notes, and example notebooks in lecture deck order
├── Coursework                  Assignment explanations and a submitted report
├── images                      Figures in the lecture notes
├── Course-Information.ko.md    Course operations
├── Course-Information.md
├── LICENSE                     Documentation license (CC BY-NC-SA 4.0)
├── LICENSE-CODE                Code license (PolyForm Noncommercial 1.0.0)
├── README.ko.md
└── README.md

The units are listed in the Lecture-Notes index, and the assignments in the Coursework index.


🤝 License

The documents in this repository (lecture notes, explanations, and summary sheets) are licensed under CC BY-NC-SA 4.0, and the code under PolyForm Noncommercial 1.0.0. Figures and example code taken from lecture slides and textbooks, as well as assignment handouts and template code distributed by the course, belong to their original authors and are not covered by these licenses.


About

This repository archives bilingual lecture notes and coursework for a software applications course on applied deep learning, covering neural network basics, CNNs and RNNs, image classification, transfer learning, object detection, sound processing, natural language processing, Transformers, LLMs, and lightweight models.

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