人工智慧正在改變不同行業，深度學習是近期人工智慧討論度最高的知識領域，本課程將透過機器學習的概念切入，了解過往資料分析探勘上的概念，統計概念、迴歸分析、機器學習、類神經網路等，並透過不同應用案例了解深度學習方法的優勢與瓶頸。本課程將以Python程式語言作為課程的案例學習的程式語言，因此，學生必須具備基礎的程式語言設計。透過AI(一)資料科學技術的基礎理論與案例示範，此課程老師與助教群提供學生手把手的專題演練，針對應用範例動手實作，提升學生AI資料分析技術理論的實作能力與經驗。此外，針對跨領域AI技術應用提出案例分享。Artificial intelligence is changing to different industries. In-depth learning is the most discussed knowledge area in recent artificial intelligence. This course will enter the concept of machine learning to understand the concepts of past data analysis and exploration, statistical concepts, epistem analysis, machine learning, and neural network, etc., and learn about the advantages and bottlenecks of deep learning methods through different application cases. This course will use Python programming language as the course's case study, so students must have a basic programming language design. Through the basic theories and case demonstrations of AI (I) data science technology, this course teacher and teaching assistant group provides students' hands-on topics, and conducts manual practices on application examples, improving students' practical ability and experience in AI data analysis technology management. In addition, we propose cases for cross-domain AI technology applications.
深度學習：
作者： Josh Patterson, Adam Gibson
譯者： 藍子軒
出版社：歐萊禮
In-depth learning:
Author: Josh Patterson, Adam Gibson
Translator: Blue Roll
Publisher: Ole Leather
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業作業 Action |
70 | |
| 課堂討論課堂討論 Class discussion |
30 |
