Convolutional Neural Networks (CNNs)
CNNs are specialized neural networks designed for processing grid-like data, such as images. They use convolution operations to automatically learn spatial hierarchies of features.
Key Components
Convolution Layer
Applies filters to detect features like edges, textures, and patterns.
Pooling Layer
Reduces spatial dimensions while retaining important information.
Fully Connected
Final layers that perform classification based on extracted features.
Simple Convolution Example
This demonstrates how a 3x3 filter slides over an image to detect features.
python
Output:
Click "Run Code" to see output
Why CNNs for Images? They preserve spatial relationships and are translation-invariant, meaning they can detect features regardless of position.