Given a learning rate of 0.01 and a gradient of 0.05, what is the update step for the weights?

In a deep learning model, the learning rate is set to 0.001. If the gradient of the loss function is 0.2, what is the update value for the weights?

Consider a function f(x)=x3−4x2+7𝑓(𝑥)=𝑥3−4𝑥2+7. What is the updated value of x𝑥 after 2nd iteration of the gradient descent update, if the learning rate is 0.10.1 and the initial value of x𝑥 is 5?

What is the RProp algorithm's learning rate update rule?Question 16Answera.The learning rate is updated based on the difference between the current and previous iteration's errorb.The learning rate is updated based on the difference between the current and previous iteration's weightsc.The learning rate is updated based on the difference between the current and previous iteration's gradientd.The learning rate is updated based on the difference between the current and previous iteration's Hessian matrix

What is the learning rate in backpropagation?Question 5Answera.The process of adjusting the weights and biases in the backward directionb.The process of minimizing the error between the predicted output and the actual outputc.The process of adjusting the weights and biases in the forward directiond.The hyperparameter that determines the size of the weight and bias updates

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