$1-$ In finding the Loss Often we need to compute the partial derivative of output with respect

a$.$ To activation function $v$ during neural network parameter learning.

b$.$ All of the above

c$.$ To wights during neural network parameter learning.

d$.$ To input during neural network parameter learning

$2-$ The scientists Minsky and Papert's

a$.$ did not have any effect on the ANN field

b$.$ Their views led to the founding of the multilayer neural networks

c$.$ Helped in flourishing the ANN field

d$.$ Had pessimistic views which held the filed back from improvements for awhile

$3-$ A neural network with any number of layers is equivalent to a single$-$layer network if we use

a$.$ Step activation function

b$.$ Tanh activation function

c$.$ All of them

d$.$ sigmoid activation function

e$.$ ReLU activation function

$4-$ In multilayer networks, the input of a node can feed into other hidden nodes, which in turn can feed into other hidden or output nodes

True

False

$5-$ For larger data sets we are better of using

a$.$ Any Al techneque

b$.$ Machine learning like SVM

c$.$ Deep Neural networks

d$.$ Neural networks like RBF

$6-$ synapses are created by

a$.$ All of the above

b$.$ Multiplying weight with the input of neurons

c$.$ connecting neurons with each others

d$.$ using the non linear activation function which helps in solving non linear problems