APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Suppose your input is a 300 by 300 color (RGB) image, and you are not using a convolutional network. If the first hidden layer has 100 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?
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9, 000, 001
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9, 000, 100
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27, 000, 001
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27, 000, 100
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Explanation:
Detailed explanation-1: -In Perceptron, there are no hidden layers, hence it takes an input and calculates the weighted input for each input node. This weighted input is then passed through an activation function to generate the output.
Detailed explanation-2: -Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.) It allows parameters learned for one task to be shared even for a different task (transfer learning). It reduces the total number of parameters, thus reducing overfitting.
Detailed explanation-3: -Step 1: Convolution. Step 1b: ReLU Layer. Step 2: Pooling. Step 3: Flattening.
There is 1 question to complete.