APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Linear Functions
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Nonlinear Functions
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Discrete Functions
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Exponential Functions
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Detailed explanation-1: -5. Neural Networks are complex with many parameters. Explanation: Neural networks are complex linear functions with many parameters.
Detailed explanation-2: -Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network.
Detailed explanation-3: -All layers of the neural network will collapse into one if a linear activation function is used. No matter the number of layers in the neural network, the last layer will still be a linear function of the first layer. So, essentially, a linear activation function turns the neural network into just one layer.
Detailed explanation-4: -Therefore, in terms of regression analysis, all neural networks are nonlinear models.