With a grasp of Flux.jl's core components from the previous chapter, we now turn to the practical application of these elements in building diverse neural network architectures. This chapter guides you through the process of constructing, from the ground up, several widely used types of neural networks.

You will learn to:

• Prepare and preprocess data specifically for deep learning models using Julia's tools, including efficient data handling with MLUtils.jl for batching and iteration.
• Build Multilayer Perceptrons (MLPs), which are foundational fully connected networks.
• Implement Convolutional Neural Networks (CNNs), essential for tasks involving grid-like data such as image processing.
• Develop Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) units, suited for sequential data like text or time series.
• Utilize embedding layers for representing categorical or textual data effectively within your networks.
• Extend Flux.jl's capabilities by creating custom neural network layers for specialized operations.
• Save your trained models using serialization techniques (e.g., with BSON.jl) and load them for later inference or continued training.

The chapter culminates in a hands-on exercise where you will apply these techniques to build and configure a CNN for an image classification problem, solidifying your understanding of how these architectural components fit together.