CSCE 636-700 Deep Learning (Online Course)
CSCE 636-700 Deep Learning (Online Course)
Instructor: Prof. Anxiao (Andrew) Jiang. Email: ajiang@cse.tamu.edu
Time and Location: Since 636-700 is an online course, it does not have a specific class meeting time. However, for the convenience of course organization, we will assume that the class has lectures on Monday, Wedensday and Friday every week, throughout the Fall Semester of 2025.
TA: Wenjing Chen (email: jj9754@tamu.edu)
Grader: Smriti (email: smriti7857@tamu.edu)
Office Hours:
Dr. Jiang's office hours: 9:00-9:30am on Mondays, via zoom: https://us04web.zoom.us/j/77100751577?pwd=hSqfM10Qav6JogvFkEdD4KktGRXAsM.1 (Meeting ID: 771 0075 1577, Passcode: 0cPD49)
TA's office hours: 3:00--4:00pm on Thursdays, and 2:00--3:00pm on Fridays, via zoom: https://tamu.zoom.us/j/94107191548?pwd=w6RvRzvENuOxdBPjqUyRmpc2i3gRIK.1 (Meeting ID: 941 0719 1548, Passcode: 187657)
Grading and Requirements:
The final grade is based on homework and project. Homework: 25%. Three projects: 25% per project (75% total).
Submission Policy: An electronic copy of each homework should be submitted in https://canvas.tamu.edu. An electronic copy of each file for Projects should be emailed to moore.research.education@gmail.com, unless notified otherwise. No late homework/project submission will be accepted. Students should double check each submission to make sure correct files are submitted. (If wrong files are submitted, correct files to replace them can only be submitted before the deadline, not after it.)
By default, all work is solo; no collaboration allowed unless stated otherwise.
Textbook:
Textbook: Deep Learning with Python, 2nd Edtion, by Francois Chollet.
Recommended Textbook: Deep Learning: Foundations and Concepts, 2024 edition, by Christopher M. Bishop and Hugh Bishop.
Thanks to the support of Texas A&M University Libraries, you can access the following course materials for free: Deep Learning with Python, https://go.oreilly.com/TAMU/library/view/-/9781617296864/?ar
Computing resources:
1. Google CoLab: an open and free Jupyter notebook environment by Google that runs in the cloud and allows us to use CPU, GPU and TPU resources. It requires no setup. It's a good resource for anyone who wants to do swift experiments in deep learning.
2. HPRC: You can apply for an account at TAMU HPRC (High Performance Research Computing). It has CPU and GPU resources.
8/25/2025 (Monday): Lecture 1: What is Deep Learning (Slides and Lecture Video).
8/27/2025 (Wednesday): Lecture 1: What is Deep Learning (Slides and Lecture Video). Lecture 2: Mathematical Building Blocks of Neural Networks. (Slides and Lecture Video).
8/29/2025 (Friday): Lecture 2: Mathematical Building Blocks of Neural Networks. (Slides and Lecture Video).
9/1/2025 (Monday): Labor day holiday. No class.
9/3/2025 (Wednesday): Lecture 2: Mathematical Building Blocks of Neural Networks. (Slides and Lecture Video).
9/5/2025 (Friday):
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10/13/2025 (Monday): Fall break. No class.
10/15/2025 (Wednesday):
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11/24/2025 (Monday):
11/26/2025 (Wednesday): Reading day. No class.
11/28/2025 (Friday): Thanksgiving holiday. No class.
12/1/2025 (Monda):
12/3/2025 (Wednesday):
12/5/2025 (Friday):
12/8/2025 (Monday):
Lecture Slides and Videos: Lecture 1: What is Deep Learning (Slides and Lecture Video). Lecture 2: Mathematical Building Blocks of Neural Networks. (Slides and Lecture Video). Lecture 3: Getting Started with Neural Networks: Classification and Regression (Slides and Lecture Video). Lecture 4: Fundamentals of Machine Learning (Slides and Lecture Video). Lecture 5: Working with Keras: A Deep Dive (Slides and Lecture Video). Lecture 6: Introduction to Deep Learning for Computer Vision (Slides and Lecture Video). Lecture 7: Advanced Deep Learning for Computer Vision (Slides and Lecture Video). Lecture 8: Deep Learning for Time Series (Slides and Lecture Video). Lecture 9: Deep Learning for Text (Slides and Lecture Video). Lecture 10: Generative Deep Learning (Slides and Lecture Video). Lecture 11: Best Practices for the Real World (Slides and Lecture Video). Lecture 12: Deep Reinforcement Learning (Slides and Lecture Video Part 1 of 3, Lecture Video Part 2 of 3, Lecture Video Part 3 of 3) Lecture 13: Deep Reinforcement Learning: Q Learning (Slides and Lecture Video) Supplementary Lectures on Linear Programming. (Lecture videos: Part 1 of 4, Part 2 of 4, Part 3 of 4, Part 4 of 4)
TBA
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