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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

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, 10/3, 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, 10/17/2024] Explainable AI using Grad-CAM for visual explanations

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

[10/22, 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, 10/31/2024] Generative Adversarial Networks

Introduction to GAN
23. GAN Notebook (part1)

Additional resources

References

Repository
Open issue

Index

By Cristiano Fanelli

© Copyright 2024.

DATA621