# concept - Deep Learning¶

## Convolutional Neural Network¶

CONV layer is a set of filters that spatially slide across input volume and output dot product during each step.

**why**: much less parameters compared to a fully connected layer, parameter sharing, sparsity of connection**convolve**: the action of sliding over width and height**local connectivity**: each filter only look at input volume within the filter window, NOT fully connected**filter**: matrix of size (f,f,c), f is mostly odd, c is the same as input volume depth (n,n,c)**depth**: hyperparameter, number of filters, depth column, or fiber**stride**: Step size when convolve the filter, mostly 1 or 2**pooling**: reduce size of representation, speeds up computation, more robust features.- hyperparameter: (f,f,s) s is step size
- output: n+2p-f+1 / s,
- Max Pooling: max(spatial filter)
- Average Pooling: less common, used to collapse

**structure**: input -> layer 1 (conv1, pool1) -> layer 2(conv2, pool2) -> layer 3(fully connected), layer 4(FC) -> softmax