Comment on the disadvantage of using linear functions as activation functions for multilayer neural networks.
What will be an ideal response?
Multilayer neural networks is useful for modeling nonlinear relation-
ships between the input and output attributes. However, if linear func-
tions are used as activation functions (instead of sigmoid or hyperbolic
tangent function), the output is still a linear combination of its input
attributes. Such a network is just as expressive as a perceptron.
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Answer the following statement true (T) or false (F)
When a document ________ an object, it maintains a pointer to the object and receives all changes made to the object by the object's server; when a document ________ an object, it copies the entire object into the document and does not receive changes made to the object by the object's server.
a) associates; controls b) links; embeds c) associates; embeds d) links; controls
A 6-megapixel camera would use smaller and more numerous photo detectors than a 3-megapixel camera
Indicate whether the statement is true or false
import java.util.Scanner; ? public class AssertTest { public static void main( String args[] ) { Scanner input = new Scanner( System.in ); ? System.out.print( "Enter a number between 0 and 10: " ); int number = input.nextInt(); ? assert ( number >= 0 && number <= 10 ) : "Invalid number: " + number; ? System.out.printf( "You entered %d\n", number ); } } ? The above code demonstrates the functionality of the assert statement. Explain what happens when an entered number is valid and when an entered number is out of range.
What will be an ideal response?