Understanding padding and strides

Up until now, we've used the default strides of one for our networks. This indicates that the model convolves one input over each axis (step size of one). However, when a dataset contains less granular information on the pixel level, we can experiment with larger values as strides. By increasing the strides, the convolutional layer skips more input variables over each axis, and therefore the number of trainable parameters is reduced. This can speed up convergence without too much performance loss.

Another that can be tuned is the padding. The padding defines how the borders of the input data (for example images) are handled. If no padding is added, only the border pixels (in the case of an image) will be included. So if you expect the borders to include valuable information, you can try to add padding to your data. This adds a border of dummy data that can be used while convolving over the data. A benefit of using padding is that the dimensions of the data...