| Model: the learnt relationship (eg., f(x; a,b) = ax + b) Parameter: an aspect of the model, a dial which is fixed by data (eg., a) Kernel (as used here): a subset of such parameters Algorithm: procedure which accepts data and produces a model Hyperparameter: an aspect of the algorithm, a dial which changes model production Convolution: A convolution of image A and Filter B describes to what degree A is "like" B. Here "Filter B" is a kernel, ie., a parameter set learnt by the network. The goal of a CNN is to produce a model whose parameters are image filters that describe the degree to which an images expresses various shapes. By learning the filters from an image set, the network is specialized to distinguish images in that set. |
This seems a bit cryptic. The way I understand hyperparamters, they define how a model learns, i.e. you can set an alpha in gradient descent. Now when you compare them to "ordinary" parameters, hyperparamters do not define relationship between data and output.