8 basic terms related to Deep Learning

Convolution is a mathematical operation on two functions (f and g) that produces a third function (f*g) expressing how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it. It is defined as the integral of the product of the two functions after one is reversed and shifted. And the integral is evaluated for all values of shift, producing the convolution function. Hyperparameter is a parameter whose value is used to control the learning process. It can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.