Course Demonstrations

EE 541: Deep Learning Foundations

Notebooks and code demos. You may copy and modify for use in EE 541.

Week 1: Introduction to Deep Learning

• Fashion-MNIST Dataset - Dataset statistics, class distribution, visualization
• Minimal PyTorch Example - Two-layer network on Fashion-MNIST, training loop
• Feature Visualization - PCA projections before and after training

Week 3: MMSE Estimation

• MMSE Estimation - Sensor estimation under Gaussian and non-Gaussian noise
• Vector LMMSE - Orthogonality verification, sample convergence, ridge regularization
• LMS Adaptive Filtering - Audio noise cancellation, step-size effects

Week 5: Classification and Logistic Regression

• Classifier Evaluation - Threshold tuning on ROC and PR curves
• Logistic Regression MLE - 4-point logistic MLE, loss surface, Newton vs GD