查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

109-2 286805 深度學習應用開發實務 1 3.0 3 ★ 賴冠廷 AI雙聯專班一 39 0 綜科211室，3/7, 3/28, 4/10, 4/17, 5/16, 5/29

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
109-2	286805	深度學習應用開發實務	1	3.0	3	★	賴冠廷	AI雙聯專班一	39	0	綜科211室，3/7, 3/28, 4/10, 4/17, 5/16, 5/29

教學大綱與進度：

教師姓名賴冠廷

Email ktlai@ntut.edu.tw

最後更新時間 2020-12-21 23:38:07

課程大綱 The goal of this course is to give students a broad overview of state-of-the-art deep learning algorithms, and teach students how to develop deep learning applications by hands-on coding examples. Most examples taught in this class are based on the TensorFlow framework. We will cover many different fields including image classification, object detection, natural language processing, reinforcement learning and generative adversarial networks. The syllabus is listed as follows: • Number and Character recognition • Image Classification for 1000 categories • Object Detection for 9000 objects • Convolutional Neural Networks (CNN) • Recurrent Neural Networks (RNN) • Long-Short Term Memory (LSTM) • Generative Adversarial Networks (GAN) • Deep Reinforcement Learning

課程進度 1. Introduction to Deep Learning (Francois (2017), Chapter 1, 2, 3) 2. Applied Math for Machine Learning (Ian et al. (2017), Chapter 2, 3, 4) 3. Fundamentals of Machine Learning (Francois (2017), Chapter 4 + Ian et al. (2017), 5) 4. TensorFlow & Keras + Word Embedding 5. Convolutional Neural Network (CNN) and Image Classification (Francois (2017), Chapter 5) 6. Recurrent Neural Network (RNN) and Long Short-term Memory (LSTM) 7. Object Detection a. YOLO 9000 – You Only Look Once b. Single-shot Multi-Box Detection 8. Generative Adversarial Networks (GAN) 9. Deep Reinforcement Learning 10. Embedded Deep Learning 11. Student Project Demos

評量方式與標準 Homework (50%), Final Project (50%)

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Francois Chollet, “Deep Learning with Python, ” Manning, 2017 Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 Latest publications on CVPR, NIPS, ICML, AAAI, ICLR

課程諮詢管道

課程對應SDGs指標

備註

教師姓名	賴冠廷
Email	ktlai@ntut.edu.tw
最後更新時間	2020-12-21 23:38:07
課程大綱	The goal of this course is to give students a broad overview of state-of-the-art deep learning algorithms, and teach students how to develop deep learning applications by hands-on coding examples. Most examples taught in this class are based on the TensorFlow framework. We will cover many different fields including image classification, object detection, natural language processing, reinforcement learning and generative adversarial networks. The syllabus is listed as follows: • Number and Character recognition • Image Classification for 1000 categories • Object Detection for 9000 objects • Convolutional Neural Networks (CNN) • Recurrent Neural Networks (RNN) • Long-Short Term Memory (LSTM) • Generative Adversarial Networks (GAN) • Deep Reinforcement Learning
課程進度	1. Introduction to Deep Learning (Francois (2017), Chapter 1, 2, 3) 2. Applied Math for Machine Learning (Ian et al. (2017), Chapter 2, 3, 4) 3. Fundamentals of Machine Learning (Francois (2017), Chapter 4 + Ian et al. (2017), 5) 4. TensorFlow & Keras + Word Embedding 5. Convolutional Neural Network (CNN) and Image Classification (Francois (2017), Chapter 5) 6. Recurrent Neural Network (RNN) and Long Short-term Memory (LSTM) 7. Object Detection a. YOLO 9000 – You Only Look Once b. Single-shot Multi-Box Detection 8. Generative Adversarial Networks (GAN) 9. Deep Reinforcement Learning 10. Embedded Deep Learning 11. Student Project Demos
評量方式與標準	Homework (50%), Final Project (50%)
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Francois Chollet, “Deep Learning with Python, ” Manning, 2017 Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 Latest publications on CVPR, NIPS, ICML, AAAI, ICLR
課程諮詢管道
課程對應SDGs指標
備註