Standard fully connected networks fail when processing high-dimensional data like images because they ignore spatial relationships. **Convolutional Neural Networks (CNNs)** solve this by looking at local pixel blocks.
The core of a CNN is the **Convolutional Layer**. Instead of connecting every pixel to every neuron, a sliding matrix called a **Kernel** (or filter) traverses the image. As it slides, it performs dot products on local region matrices, highlighting spatial features like edges, textures, or shapes.
**Pooling** layers reduce the spatial size of representation matrices to cut down parameters and computational load. The most popular method is **Max Pooling**, which slides a filter across the output and selects the maximum value from each block, keeping only the most dominant features.
Calculate the output spatial size (O) for a convolution operation with the following settings:
Next, we will apply convolutional concepts in a project where we train a model on real images!