這門課程主要介紹「深度學習」所需處理的各類問題，以及所使用的分析方法和模型。課程將以簡單的概念與理論講解各類方法與模型，並以 Python 使用 tensorflow 和 pytorch 進行演示。課程結束後，學生們將能夠運用「深度學習」的方法進行分析與建模。This course mainly introduces the various problems required for "deep learning", as well as the analytical methods and models used. The course will explain various methods and models in simple concepts and theories, and demonstrate them in Python using tensorflow and pytorch. After the course is over, students will be able to use the "deep learning" method to analyze and model.
本課程旨在提供學生深入了解並實際操作深度學習技術的機會，課程將涵蓋深度學習的基本概念與核心技術。同時，透過實作環節，學生將學習如何使用工具如 Keras 和 PyTorch 等，應用於圖像辨識、自然語言處理等資料中。本課程適合想提升實作技能的學生，最終幫助學生掌握設計和優化深度學習模型的能力，應用於各種實際問題。
This course aims to provide students with opportunities to gain insight and implement deep learning techniques, which will cover the basic concepts and core technologies of deep learning. At the same time, through the implementation cycle, students will learn how to use tools such as Keras and PyTorch to apply them to image identification, natural language processing and other materials. This course is suitable for students who want to improve their practical skills, and ultimately helps students master the ability to design and optimize deep learning models, and is applied to various practical problems.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業作業 Action |
30 | |
| 期末報告期末報告 Final report |
30 | |
| 期末競賽期末競賽 Final competition |
30 | |
| 出席狀況與平時表現 出席狀況與平時表現 Attendance and performance during normal times |
10 |
