You are given:

n data samples xi = (x1,i, , xp,i), i = 1, . . . , n

n corresponding to true responses (or lables) yi , i = 1, . . . , n.

and asked to train a single linear neuron network to approximate function f(.) such that $f(x_i) = y_i), i = 1, . . . ,n $. Provided the train steps for your network by answering the following questions.

a) What is the formula to calculate an output yi from an input xi? What are the model parameters in that formula?

b) What criteria do we need to optimize in order to estimate the model parameters?

c) What is the name of the method used to optimize this criteria in case you do not have access to an analytical solution?

data science

You are given: n data samples Xi = (21.12 --- , Ipi), i = 1,...,n . n corresponding to true responses (or lables) y, i = 1,...,n. and asked to train a single linear neuron network to approximate function f(.) such that $f(x_i) =y_i), i = 1, ...,n $. Provided the train steps for your "network by answering the following questions. a) What is the formula to calculate an output n from an input x? What are the model parameters in that formula? b) What criteria do we need to optimize in order to estimate the model parameters? c) What is the name of the method used to optimize this criteria in case you do not have access to an analytical solution? You are given: n data samples Xi = (21.12 --- , Ipi), i = 1,...,n . n corresponding to true responses (or lables) y, i = 1,...,n. and asked to train a single linear neuron network to approximate function f(.) such that $f(x_i) =y_i), i = 1, ...,n $. Provided the train steps for your "network by answering the following questions. a) What is the formula to calculate an output n from an input x? What are the model parameters in that formula? b) What criteria do we need to optimize in order to estimate the model parameters? c) What is the name of the method used to optimize this criteria in case you do not have access to an analytical solution