--- title: Convolutional Neural Networks Lecture categories: lecture --- # Briefing ## What is a Convolutional Network + Convolutional Layers + $3\times3$ or $5\times5$ (possibly $7\times7$) + Pooling + Often maximum; sometimes average or other functions + Convolution-Detector-Pooling = Convolutional Unit + Convolution + Activation (Detector) + Pooling Why is convolution suitable for images? ## Features of CNN + Capacity (degrees of freedom) + fewer weights (DOF) per layer + Representation learning - may learn edge or corner detection for instance + Location invariance + Hierarchies; serial and parallel modularisation + Fully connected layer at the end. - convolutional units learn features - last layer uses the features like a traditional ANN with one hidden layer ## Considerations in CNN + Padding (edge conditions for convolution) + Batch Normalisation + mini batches + Filter/Kernel ## Other important features (any ANN) + Normalisation + Over- and under-training + Plot over epochs + Feature detection