CSCE 636-600 Deep Learning
CSCE 636-600 Deep Learning
Instructor: Prof. Anxiao (Andrew) Jiang. Email: ajiang@cse.tamu.edu
Time and Location: 8/25/2025 to 12/16/2025, 10:20--11:10am on Monday, Wednesday and Fridays, in 124 HRBB
TA: Wenjing Chen (email: jj9754@tamu.edu)
Grader: Smriti (email: smriti7857@tamu.edu)
Office Hours:
Dr. Jiang's office hour: 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.
8/27/2025 (Wednesday): Lecture 1: What is Deep Learning. Lecture 2: Mathematical Building Blocks of Neural Networks.
8/29/2025 (Friday): Lecture 2: Mathematical Building Blocks of Neural Networks.
9/1/2025 (Monday): Labor day holiday. No class.
9/3/2025 (Wednesday): Lecture 2: Mathematical Building Blocks of Neural Networks.
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10/13/2025 (Monday): Fall break. No class.
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11/26/2025 (Wednesday): Reading day. No class.
11/28/2025 (Friday): Thanksgiving holiday. No class.
12/1/2025 (Monda):
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12/8/2025 (Monday):
Lecture Slides: Lecture 1: What is Deep Learning. Lecture 2: Mathematical Building Blocks of Neural Networks. Lecture 3: Getting Started with Neural Networks: Classification and Regression. Lecture 4: Fundamentals of Machine Learning. Lecture 5: Working with Keras: A Deep Dive. Lecture 6: Introduction to Deep Learning for Computer Vision. Lecture 7: Advanced Deep Learning for Computer Vision. Lecture 8: Deep Learning for Time Series. Lecture 9: Deep Learning for Text. Lecture 10: Generative Deep Learning. Lecture 11: Best Practices for the Real World. 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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