# Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing grid-like data such as images.
## Architecture
### Convolutional Layers
Convolutional layers apply filters (kernels) to detect features:
- **Feature Detection**: Identifies patterns like edges, textures
- **Parameter Sharing**: Reduces number of parameters
- **Spatial Invariance**: Detects features regardless of position
### Pooling Layers
Pooling layers reduce spatial dimensions:
- **Max Pooling**: Takes maximum value in each region
- **Average Pooling**: Takes average value
- **Purpose**: Reduces computation and overfitting
### Fully Connected Layers
Final layers that perform classification or regression based on extracted features.
## Popular Architectures
### LeNet
Early CNN for digit recognition.
### AlexNet
Breakthrough architecture that won ImageNet 2012.
### VGG
Deep network with small 3x3 filters.
### ResNet
Residual connections enable very deep networks (100+ layers).
## Applications
CNNs excel at:
- **Image Classification**: Identifying objects in images
- **Object Detection**: Locating and classifying objects
- **Semantic Segmentation**: Pixel-level classification
- **Medical Imaging**: Disease detection and diagnosis
## Training Considerations
- **Data Augmentation**: Increases dataset diversity
- **Transfer Learning**: Using pre-trained models
- **Regularization**: Dropout, batch normalization
## Conclusion
CNNs revolutionized computer vision and remain the foundation of modern image analysis systems.