Introduction

In the previous chapter, we explored the foundational concepts behind neural networks and saw how they can approximate functions using layers of interconnected neurons. However, standard feedforward networks struggle with high-dimensional data like images, where preserving spatial relationships is essential. We now shift our focus to Convolutional Neural Networks (CNNs), a specialized type of neural network designed to process and interpret image data effectively.

CNNs revolutionized the field of computer vision and are now the backbone of many modern applications, including facial recognition, medical image analysis, and autonomous driving.

Unlike fully connected neural networks, CNNs leverage three key ideas:

• Local Receptive Fields (Convolution operations)

• Parameter Sharing (Reduces the number of trainable weights)

• Spatial Hierarchies (Pooling layers for downsampling)

These properties make CNNs translation-invariant and computationally efficient compared to dense neural networks when dealing with high-dimensional data like images.