Variational Autoencoder Code and Experiments
Detailed walkthrough of a PyTorch VAE implementation trained on MNIST, including visualizations of data generation, reconstruction, and anomaly detection
Detailed walkthrough of a PyTorch VAE implementation trained on MNIST, including visualizations of data generation, reconstruction, and anomaly detection
Introduction to latent variable models, derivation of the ELBO, and the relationship with Expectation Maximization
Intuitive introduction to KL divergence, including discussion on its asymmetry
Code-based walkthrough showing how AUC is computed for a simple model trained on a simple dataset