A Hands-on Demo of Autoencoders

Autoencoders are powerful tools in machine learning, useful for tasks such as dimensionality reduction, anomaly detection, data denoising, and detecting multivariate covariate shifts. The post provides a hands-on demo using PyTorch Lightning to train an autoencoder, explaining the key components (encoder and decoder) and their roles. It highlights how to implement and train the model, alongside useful training optimizations like epoch and batch iteration, checkpoint saving, and multi-GPU support. Autoencoders are essential for addressing covariate shift problems in real-world ML models.