Overview
CNNs are the foundation of modern computer vision. They use 'convolutional layers' that apply filters to the input to detect patterns like edges, textures, and eventually complex objects.
Key Components
- Convolutional Layers: Extract features using sliding filters (kernels).
- Pooling Layers: Reduce the spatial dimensions (downsampling) to make the model more efficient and robust.
- Fully Connected Layers: Perform the final classification based on the extracted features.
Applications
- Image classification and object detection.
- Facial recognition.
- Medical image analysis.