APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Weights are the input, biases are extra values you add to the output
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Weights are the multiplier, biases are extra values you add to the output
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Weights are the multiplier, biases are the inputs
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Weights are the extra values added to the output, biases are the multiplier
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Detailed explanation-1: -Weight is the parameter within a neural network that transforms input data within the network’s hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value.
Detailed explanation-2: -Basically, biases are updated in the same way that weights are updated: a change is determined based on the gradient of the cost function at a multi-dimensional point. Think of the problem your network is trying to solve as being a landscape of multi-dimensional hills and valleys (gradients).
Detailed explanation-3: –The output layer is the final layer in the neural network where desired predictions are obtained. There is one output layer in a neural network that produces the desired final prediction. It has its own set of weights and biases that are applied before the final output is derived.