Following our introduction to autoencoders and their objectives, this chapter focuses on their structural composition. We will dissect the architecture into its two main parts: the encoder and the decoder. You'll see how the encoder processes input data to create a compressed, lower-dimensional summary, and how the decoder then uses this summary to try and recreate the original input.

Specifically, we will cover:

• The encoder's task: reducing data dimensionality, and the typical layers involved.
• The bottleneck (or latent space): its function as the layer holding the compressed data representation, often denoted as $z$.
• The decoder's task: reconstructing the data from $z$, and its generally symmetrical structure to the encoder.
• Activation functions, such as $ReLU$ for hidden layers and $Sigmoid$ for output layers, and their specific roles.
• The complete path of data, from an input $X$, through the encoder to $z$, and through the decoder to the reconstructed output $X'$.

By understanding these core architectural elements, you will be better prepared to see how autoencoders learn useful data representations and achieve tasks like dimensionality reduction.