Question

Question 29
? On what parameters can change in weight vector depends.
Options (Marks: 1)​

Answer 1

Answer:

a) learning parameters

b) input vector

c) learning signal.

Answer 2

Answer:

learning parameters, input vector, and learning signal parameters can change in weight vector depends

Explanation:

• A neural network's weight parameter alters input data in the network's hidden layers.
• A network of neurons or nodes makes up a neural network.
• A set of inputs, a weight, and a bias value are contained within each node.
• An input is multiplied by a weight value when it enters the node, and the resulting output is either observed or forwarded to the neural network's next layer.
• The hidden layers of a neural network frequently contain the weights of the network.
• To comprehend how weights operate, it can be beneficial to visualize a theoretical neural network. An input layer of a neural network receives the input signals and passes them on to the subsequent layer.
• Next, a number of hidden layers in the neural network perform modifications to the input data. The weights are applied within the nodes of the hidden layers. A single node might, for instance, take the incoming data, multiply it by a given weight value, and then apply a bias before sending the results to the following layer. The output layer, the last layer of the neural network, is another name for it. In order to create the desired numbers, the output layer frequently adjusts the inputs from the hidden layers.
• These parameters can change in weight vector depending on the following:
• learning parameters
• input vector
• learning signal