Exercise 1
Suppose you had a neural network with linear action functions. That is, for each unit the output is some constant c times the weighted sum of the inputs.
Exercise 2
Using the data from the previous exercises, investigate the use of
nnet
from the homonymous package in R to learn a neural network.
The function provides an implementation to fit single-hidden-layer
neural networks, possibly with skip-layer connections (i.e., a link from
the input node directly to the output nodes). In particular, use that
function for both regression and binary classification and become
acquainted with the changes that they the two cases require. For
multinomial classification try the function multinom
from the
same function. Start by looking at the examples. Finally, compare the
performance against the previous linear regression models via glm
and mlogit
.