The network that involves backward links from output to the input and hidden layers is called

Related MCQs

• What are the steps for using a gradient descent algorithm? 1)Calculate error between the actual value and the predicted value 2)Reiterate until you find the best weights of network 3)Pass an input through the network and get values from output layer 4)Initialize random weight and bias 5)Go to each neurons which contributes to the error and change its respective values to reduce the error
• What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned
• What is the sequence of the following tasks in a perceptron? Initialize weights of perceptron randomly Go to the next batch of dataset If the prediction does not match the output, change the weights For a sample input, compute an output
• The problem of finding hidden structure in unlabeled data is called…
• If machine learning model output involves target variable then that model is called as
• If machine learning model output doesnot involves target variable then that model is called as
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?