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 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

On what parameters can change in the weight vector depend?Question 31Answera. Input vector.b.Learning signalc.Learning parametersd.All

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 the weight update rule of the neural network, w  = w - a \delta w1 pointa is called learning ratea is called Hebbian constanta is called momentumNone of the above

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 adjust the weights of the neural network based on the input and outputc.It is used to calculate the output of the neural networkd.It is used to determine the input to the neural network