Skip to main content

Ctrl+K

This page is being updated

Welcome to DATA621 - Neural Networks and Deep Learning

Syllabus

Syllabus (last update 10/6/2024)

Schedule

Schedule

Assignments

Assignment 1, Deep Neural Networks from Scratch
Assignment 2, Convolutional Neural Networks
Assignment 3, Generative Adversarial Networks
Assignment 4, Graph Neural Networks

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

[8/29/2024] Introduction to High Performance Computing (HPC)

4. Introduction to High Performance Computing

[9/3/2024] Linear Classifiers

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

[9/5/2024] Gradient Descent, stochastic GD, Logistic Regression and Other classifiers

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

[9/10/2024] Building Training Datasets

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

[9/12/2024] Model Evaluation, Hyperparameter Tuning: Examples and Discussion

10. Model Evaluation, Hyperparameter Tuning (Jupyter notebook)

[9/17/2024, 9/19/2024] Building Multi-Layer Neural Networks from scratch + discussion on assignment

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

[9/24/2024] Building Multi-Layer Neural Networks with PyTorch

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

[9/26/2024] PyTorch and Autograd

14. Using PyTorch Autograd (Jupyter notebook)

[10/1/2024, 10/3/2024, 10/8/2024] CNN with PyTorch

15. Using PyTorch with CNN
16. Using CNN and Celeb Dataset
17. Using CNN for Regression Tasks (age prediction based on images)
18. Using U-Net for Image Segmentation

[10/15/2024, 10/17/2024] Explainable AI using Grad-CAM for visual explanations

19. Introduction to Grad-CAM
20. Grad-CAM (part 2)

[10/22/2024, 10/24/2024] Recurrent Neural Network

21. 2024 Nobel Prize in Physics: Hopfield NN and Boltzmann Machines
Introduction to Sequential Data and Time Series Data; Intro to RNN
22. LSTM Recurrent Neural Networks

[10/29/2024, 10/31/2024, 1/7/2024] Generative Adversarial Networks

Introduction to GAN
23. GAN Notebook (part1)
24. DCGAN and Wasserstein GAN
25. Conditional GAN

[11/24/2024] Graph Neural Networks

26. GNN Notebook (part1)
27. GNN Notebook (part2)

[12/03/2024, 12/05/2024] Transformers

28. Introduction to Transformers

Additional resources

References

Repository
Open issue

Index

By Cristiano Fanelli

DATA621