Convolutional Neural Networks

CNN Architecture

CNNs excel at image recognition with parameter sharing.

Convolutional Layers

Conv2D(filters, kernel_size) applies learnable filters. Stride and padding control output size.

Filters detect features like edges, textures. Multiple filters learn different features.

Pooling Layers

MaxPooling2D(pool_size) reduces spatial dimensions. AveragePooling averages values.

Pooling reduces parameters and provides translation invariance.

CNN Architecture

Typical: Conv-Pool-Conv-Pool-Flatten-Dense. More filters as spatial size decreases.

Transfer learning uses pre-trained models (VGG, ResNet) as feature extractors.

Key Takeaways

1. Conv layers detect spatial features
2. Pooling reduces dimensions
3. Transfer learning leverages pre-trained models