Machine Learning : Introduction to Variational Autoencoders

Implement an autoencoder with encoder and decoder neural networks using PyTorch in Google Colab, training to reconstruct inputs from a latent space.

Build a training loop for a variational autoencoder, training an encoder and decoder with Adam optimizers, tracking mean squared error loss and progress with tqdm.

Examine how a variational autoencoder compresses a 512×512 image into a four-value latent vector Z, reconstructs it with a decoder, and compare decoder parameters to latent storage.

Load the MNIST dataset and train an encoder decoder on 28x28 images with mini-batches to compress and reconstruct digits, illustrating amortization by storing only the decoder for new inputs.

Explore the latent space Z of test images by encoding, then visualize a 2D t-sne embedding to observe digit label clustering and interpolation, before introducing variational autoencoders.

Explore how variational autoencoders use an encoder to model P(Z|X) as a Gaussian with a mean and log variance, and a decoder to reconstruct X with a KL regularizer.

Explore a PyTorch implementation of a variational autoencoder, including encoder‑decoder networks, reparameterization, KL divergence, and training on 0‑1 normalized data with a 2‑D latent space.

Wrap up the variational autoencoders discussion and invite constructive feedback to improve this course, while noting interest in a follow-up autoencoders course.