The previous chapters detailed the core principles of autoencoders, covering their architecture and learning mechanisms. This chapter transitions from those principles to practical application. Here, we offer a step-by-step guide to building your first basic autoencoder.

You will learn to:

•    Set up a Python environment suitable for deep learning tasks.
•    Use PyTorch and Keras 3 for defining and constructing neural network models.
•    Load, understand, and preprocess a common dataset, like MNIST, for training an autoencoder.
•    Construct the encoder and decoder layers of a simple autoencoder.
•    Configure the training process, including selecting an optimizer and a loss function such as Mean Squared Error (MSE). MSE measures the average squared difference between the original input $x$ and the reconstructed output $\\hat{x}$, commonly expressed as $L(x, \hat{x}) = \frac{1}{N} \sum_{i=1}^{N} (x_i - \hat{x}_i)^2$
•    Train the autoencoder and then assess its ability to reconstruct data.
•    Visualize original inputs alongside their reconstructed versions to evaluate performance.

By working through this chapter, you will gain direct experience in implementing an autoencoder and observing its data reconstruction capabilities. This practical exercise will solidify your understanding of how these networks operate.