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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

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

Principles of Information Systems (MindTap Course List)
Principles of Information Systems (MindTap Course List)
12th Edition
ISBN:9781285867168
Author:Ralph Stair, George Reynolds
Publisher:Ralph Stair, George Reynolds
Chapter3: Hardware: Input, Processing, Output, And Storage Devices
Section: Chapter Questions
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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
Transcribed Image Text: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
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