Implementation of Stacked Autoencoder Exercise from Stanford Deep Learning Tutorial

The Stanford Deep Learning Tutorial includes a practical exercise focused on stacked autoencoders. This implementation addresses the original incomplete codebase by filling all missing code segments to create a fully functional neural network. The complete solution now includes proper weight initialization, forward propagation algorithms, and backpropagation optimization routines. To execute the code successfully, users need to place the handwritten digit recognition dataset (typically MNIST or similar) into the specified directory path. Key implementation aspects include layer-wise pretraining using greedy algorithms, fine-tuning through supervised learning, and feature extraction mechanisms that enable efficient dimensionality reduction. The enhanced codebase demonstrates hierarchical feature learning where each autoencoder layer captures increasingly abstract representations of the input data, ultimately achieving improved classification performance through deep network architecture.