Feature is the dimension that describes the characteristics of the sample. Regarding its interpretability in traditional machine learning and deep learning, the following statement is correct:

A scalar k is multiplied by matrix A equal to k and each of the numbers in A is multiplied.

Only matrix A and matrix B have the same number of rows and columns. A and B can be added.

When training the network, we often encounter many problems. For the problem of gradient disappearance, we can alleviate the problem by choosing which of the following functions to use?

Generating a confrontation network is like a game system. The generator generates fake samples, and the discriminator judges whether it is true or false.
Our ideal result is?