教師於課堂中引導式講授目前國際發展最先進之深度學習方法學及其應用，帶領學生原理介紹、數學推導實務應用，熟悉使用深度學習。
(1) 了解深度學習技術的數學基礎
(2) 熟悉深度學習工具（例如 PyTorch、TensorFlow 等）
(3) 探討深度學習技術的最新發展及其應用In the classroom, teachers teach the current leading in-depth learning methods and applications of international development, and lead students to introduce principles and mathematics to promote practical applications, and be familiar with using in-depth learning.
(1) Understand the mathematical foundations of deep learning technology
(2) Familiar with in-depth learning tools (such as PyTorch, TensorFlow, etc.)
(3) Explore the latest developments and applications of in-depth learning technology
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.
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st Ed.,MIT Press, Dec. 2016
2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd edition, Nov. 2018
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st Ed.,MIT Press, Dec. 2016
2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd edition, Nov. 2018
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 4 Labs (done individually)4 Labs (done individually) 4 labs (done individually) |
80 | |
| Final examFinal exam final exam |
20 |
