What is the hidden layer in the backpropagation algorithm?Select one:a.The layer that is not visible to the user and processes the input datab.The layer that determines the activation function of the neural networkc.The layer that is visible to the user and processes the input datad.The layer that produces the final output of the neural network
Question
What is the hidden layer in the backpropagation algorithm?Select one:a.The layer that is not visible to the user and processes the input datab.The layer that determines the activation function of the neural networkc.The layer that is visible to the user and processes the input datad.The layer that produces the final output of the neural network
Solution
The hidden layer in the backpropagation algorithm is:
a. The layer that is not visible to the user and processes the input data
This layer is called "hidden" because it's not directly exposed to the input or output. It's where the intermediate processing or computations take place. The hidden layer takes the inputs from the input layer, applies weights, and passes them through an activation function before passing the results to the output layer.
Similar Questions
What are the hidden layers of a feedforward neural network called?Select one:a.Input layersb.Hidden layersc.Output layersd.None of the above
What is the backpropagation algorithm used for?Question 10Answera.To optimize the activation function of a neural networkb.To find the optimal weights and biases of a neural networkc.To classify data using a neural networkd.All of the above
Which one of the following is First layer of Neural Network?Outer LayerHidden LayerInput LayerUpper LayerNone of above
What is a backpropagation network?Select one:a.A type of artificial neural network that uses deep learningb.A type of artificial neural network that uses unsupervised learningc.A type of artificial neural network that uses reinforcement learningd.A type of artificial neural network that uses supervised learning
Question textHow does a backpropagation network learn?Select one:a.By adjusting the number of neurons in the network based on the input and output datab.By adjusting the weights and biases of the network based on the input and output datac.By adjusting the activation function of the network based on the input and output datad.By adjusting the architecture of the network based on the input and output data
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