What is the role of the learning rate (η) in the Hebb rule?Select one:a.It determines the size of the weight updateb.It determines the convergence of the weight updatec.It determines the direction of the weight updated.It determines the speed of the weight update
Question
What is the role of the learning rate (η) in the Hebb rule?Select one:a.It determines the size of the weight updateb.It determines the convergence of the weight updatec.It determines the direction of the weight updated.It determines the speed of the weight update
Solution
The role of the learning rate (η) in the Hebb rule is:
a. It determines the size of the weight update
The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. If the learning rate is low, the model will learn slowly. On the other hand, if it is high, the weights could potentially skip the optimal solution.
Similar Questions
How is the Hebb rule used in the training of a neural network?Select one:a.It is used to determine the structure of the neural networkb.It is used to calculate the output of the neural networkc.It is used to adjust the weights of the neural network based on the input and outputd.It is used to determine the input to the neural network
What is the main advantage of the Hebb rule?Select one:a.It is easy to implementb.It is able to handle nonlinear relationshipsc.It is able to handle large datasetsd.It is fast to converge
Hebb’s Law can be represented in the form of two rules:If two neurons on either side of a connection (synapse) are activated synchronously, then the weight of that connection is increased.If two neurons on either side of a connection (synapse) are activated asynchronously, then the weight of that connection is decreased.Learning according to Hebb’s Law is primarily consistent with one of the following kinds of learning. Reinforcement learning Un-supervised learning. Supervised learning
In Hebbian learning, what does the weight adjustment depend on? a. Correlation between input and output b. The number of layers in the network c. The learning rate d. The transfer function used
What is the equation for the Hebb rule?Select one:a.w(new) = w(old) + η(input - output)x(target)b.w(new) = w(old) + η(target - output)x(input)c.w(new) = w(old) + η(output)x(input)d.w(new) = w(old) + η(output - target)x(input)
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