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7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove

7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove

MIS
MIS
9th Edition
ISBN:9781337681919
Author:BIDGOLI
Publisher:BIDGOLI
Chapter13: Intelligent Information Systems
Section: Chapter Questions
Problem 5AYRM
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1. Please check the answer and add explanation properly and solve all questions. 2. Give reasons for incorrect options also
7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove
Transcribed Image Text:7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove
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