COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Input
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Hidden Layer
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Output
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None of the above
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Detailed explanation-1: -The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.
Detailed explanation-2: -Neural Networks are not dumps of memory as we see on the computer. There are no addresses where a particular chunk of memory resides. All the neurons together make sure that a given input leads to a particular output.
Detailed explanation-3: -Activation functions are applied to the weighted sum of inputs called z (here the input can be raw data or the output of a previous layer) at every node in the hidden layer(s) and the output layer. Today, we’re going to discuss the following different types of activation functions used in neural networks.