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

110-1 299648 深度學習應用開發實務 1 3.0 3 ★ 賴冠廷電資外生四 12 3 ◎

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-1	299648	深度學習應用開發實務	1	3.0	3	★	賴冠廷	電資外生四	12	3	◎

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 2021-09-16 23:42:14

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 (CNNs) • Recurrent Neural Networks (RNN) • Long-Short Term Memory (LSTM) • Transformer, BERT, and GPT-3 • Generative Adversarial Networks (GANs) • Deep Reinforcement Learning

課程進度 1. Intro to Deep Learning 2. Applied Math (Linear Algebra, Probability, Calculus) 3. Machine Learning Basics 4. TensorFlow & Keras 5. Convolutional Neural Networks 6. Advanced Keras API 7. NLP & Word Embedding 8. RNN/LSTM and Stock Price Prediction 9. Attention & Transformer, BERT, GPT-3 10. Generative Adversarial Networks (GANs) 11. Object Detection and Segmentation 12. Deep Reinforcement Learning

評量方式與標準 Homework (40%), Midterm and Final Exam (30%) Final Project (30%)

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
1. Francois Chollet, “Deep Learning with Python, ” Manning, 2017 2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 3. Latest publications on CVPR, NIPS, ICML, AAAI, ICLR Website: http://www.aiotlab.org/teaching/dl_app.html

課程諮詢管道 ktlai@ntut.edu.tw

課程對應SDGs指標

備註 ●上課方式：遠距上課

●評量方式：
All quizzes, exams and homework are online. For final project, students need to submit a YouTube video.
●補充說明資訊：
First week online class:
Building Deep Learning Applications
Thursday, 23 September · 3:00 – 6:00pm
Google Meet joining info
Video call link: https://meet.google.com/jup-hfsy-pjw

教師姓名	賴冠廷
Email	ktlai@ntut.edu.tw
最後更新時間	2021-09-16 23:42:14
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 (CNNs) • Recurrent Neural Networks (RNN) • Long-Short Term Memory (LSTM) • Transformer, BERT, and GPT-3 • Generative Adversarial Networks (GANs) • Deep Reinforcement Learning
課程進度	1. Intro to Deep Learning 2. Applied Math (Linear Algebra, Probability, Calculus) 3. Machine Learning Basics 4. TensorFlow & Keras 5. Convolutional Neural Networks 6. Advanced Keras API 7. NLP & Word Embedding 8. RNN/LSTM and Stock Price Prediction 9. Attention & Transformer, BERT, GPT-3 10. Generative Adversarial Networks (GANs) 11. Object Detection and Segmentation 12. Deep Reinforcement Learning
評量方式與標準	Homework (40%), Midterm and Final Exam (30%) Final Project (30%)
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 1. Francois Chollet, “Deep Learning with Python, ” Manning, 2017 2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 3. Latest publications on CVPR, NIPS, ICML, AAAI, ICLR Website: http://www.aiotlab.org/teaching/dl_app.html
課程諮詢管道	ktlai@ntut.edu.tw
課程對應SDGs指標
備註	●上課方式：遠距上課 ●評量方式： All quizzes, exams and homework are online. For final project, students need to submit a YouTube video. ●補充說明資訊： First week online class: Building Deep Learning Applications Thursday, 23 September · 3:00 – 6:00pm Google Meet joining info Video call link: https://meet.google.com/jup-hfsy-pjw