Description

This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction to classical machine learning and review some key concepts required to understand deep learning. In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as image and natural language processing. Various CNN and RNN models will be covered. In the third part, we introduce deep reinforcement learning and its applications.

This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and PyTorch will be used and explained in details.

Syllabus

Instructor

Teaching Assistants

Mail: dl2026@datalab.cs.nthu.edu.tw

Prerequisites

This course is intended for senior undergraduate and junior graduate students who have a proper understanding of

• Python Programming Language
• Calculus
• Linear Algebra
• Probability Theory

Although it would be helpful, knowledge about classical machine learning is NOT required.

IMPORTANT

Students will group (2~4 people a group). This class requires each group of students to prepare a GPU card to perform the necessary computing. You can follow this link to decide which GPU card to go for. NO GPU CARD PROVIDED IN THE CLASS.

Curriculum

If you have any feedback, feel free to contact: shwu [AT] cs.nthu.edu.tw

Lecture 01

Lecture 02

Lecture 03

Lecture 04

Resources

Following provides links to some useful online resources. If this course starts your DL journey, don't stop here. Enroll yourself in advanced courses (shown below) to learn more.

Other Course Materials

For more course materials (such as assignments, score sheets, etc.) and online forum please refer to the eeclass system.

eeclass System

Reference Books

• Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016, ISBN: 0387848576

• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 2009, ISBN: 0387848576

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 0387310738

• Sebastian Raschka, Python Machine Learning, Packt Publishing, 2015, ISBN: 1783555130

Online Courses

• CS231n: Convolutional Neural Networks for Visual Recognition, Stanford

• CS224d: Deep Learning for Natural Language Processing, Stanford

• CS285: Deep Reinforcement Learning, Berkeley

• MIT 6.S094: Deep Learning for Self-Driving Cars, MIT