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Welcome to Neural Networks and Deep Learning 2025 (NNDL-2025)

Syllabus

Syllabus (last update 1/23/2025)

Schedule

Schedule

Assignments

Assignment 1, Deep Neural Networks from Scratch
Solution to Assignment 1, by Steven Morse
Assignment 2, Convolutional Neural Networks
Solution to Assignment 2, by Kaibu Feng
Assignment 3, Generative Adversarial Networks
Solution to Assignment 3, by Tyson Walsh
Assignment 4, Graph Neural Networks
Solution to Assignment 4, by Kaibu Feng

Intro

Welcome and Intro by Prof. C.Fanelli

[Pre-flight] Intro to Modeling

Linear Regression (lecture slides)
1. Linear Regression (Jupyter Notebook)

[Pre-flight] Introduction to git

2. Brief course on git
3. Exercises on git

[Pre-flight] Using VS-Code

Installing Visual Studio Code

[Pre-flight] Introduction to High Performance Computing (HPC)

4. Introduction to High Performance Computing
Other Resources: Distributed ML @ W&M
Other Resources: Student Manual for using the HPC @ W&M for Data Science

[Pre-flight] Linux Commands

Linux Commands

[1/28/2025] Linear Classifiers

Perceptrons (lecture slides)
Perceptron - Proof of Convergence (PDF)
5. Training a Perceptron (Jupyter notebook)
Adaline (lecture slides)
6. Training Adaline (Jupyter notebook)

[1/30/2025] Gradient Descent, stochastic GD, Logistic Regression and Other classifiers

Stochastic Gradient Descent (lecture slides)
7. Gradient Descent, Stochastic Gradient Descent, Mini-batch GD (Jupyter notebook)
An Intro to Logistic Regression, Regularization, linear (and non-linear) boundaries (lecture slides)
8. Logistic Regression, Regularization, linear (and non-linear) boundaries (Preview)

[2/4/2025] Building Training Datasets

Building Training Datasets (lecture slides)
9. Data Preprocessing (Jupyter notebook)

[2/6/2025] Regularization (Continued, see 1/30/2025)

10. Logistic Regression, Regularization, linear (and non-linear) boundaries (Preview)

[2/11/2025] Model Evaluation, Hyperparameter Tuning: Examples and Discussion

11. Model Evaluation, Hyperparameter Tuning (Jupyter notebook)

[2/13/2025] Building Multi-Layer Neural Networks from scratch + discussion on assignment

Adding hidden layers + Math of Backprop (lecture slides)
12. Multi-Layer Perceptron (Jupyter notebook)

[2/18/2024] Building Multi-Layer Neural Networks with PyTorch

13. Using PyTorch preflight (Jupyter notebook)
14. Using torch.nn (Jupyter notebook)

[2/25 - 2/27 2025] PyTorch and Autograd

15. Using PyTorch to build Neural Networks (continue)
17. Autograd with PyTorch (continue)

[3/4 - 3/6 2025] CNN with PyTorch

18. Using PyTorch with CNN
19. Using CNN and Celeb Dataset
(A really nice and well-known resource:) Interactive Node-Link Visualization of Convolutional Neural Networks by Adam Harley

[3/18/2025] Explainable AI using Grad-CAM for visual explanations

20. Explainable AI (XAI) and Grad-CAM

[3/20/2025] CNN: from Classification to Regression Tasks

21. age prediciton based on face images

[3/25 - 3/26 - 4/1 - 2025] Recurrent Neural Network

Introduction to Sequential Data and Time Series Data; Intro to RNN
22. LSTM Recurrent Neural Networks (updated)

[4/3 - 4/8 - 4/10 - 4/15 - 2025] Generative Adversarial Networks

Introduction to GAN
23. Vanilla GAN Notebook
24. Deep Convolutional Generative Adversarial Network (DCGAN)
25. Wasserstein GAN (WGAN)
26. Conditional DCGAN (cDCGAN)

[4/15 - 4/17 - 4/22 - 4/24 2025] Graph Neural Networks

27. An Intorduction to GNN (part1)
28. GNN Notebook (part2)

Additional resources

References

Repository
Open issue

Index

By Cristiano Fanelli

NNDL_2025