While standard fully connected networks process input features independently, they struggle with spatial hierarchies found in data like images. This chapter introduces Convolutional Neural Networks (CNNs), a specialized architecture designed to effectively learn from grid-like data by preserving and analyzing spatial relationships.

You will learn the fundamental building blocks of CNNs and how to implement them using Keras:

• Convolutional Layers ($Conv2D$): Understand how filters (kernels) slide across input data to detect patterns like edges, textures, and shapes, generating feature maps.
• Pooling Layers ($MaxPooling2D$): Grasp how pooling reduces the spatial dimensions of feature maps, making the model more robust to variations in object position and reducing computational load.
• Building CNN Architectures: Learn to stack convolutional and pooling layers, followed by Flatten and Dense layers, to create models capable of tasks like image classification.
• Working with Image Data: Explore Keras utilities for loading, preprocessing, and augmenting image datasets.
• Practical Implementation: Apply these concepts by constructing and training a CNN model on a standard image dataset.

By the end of this chapter, you will be able to build, train, and understand the basic workings of CNNs for image-related tasks.