What is the gradient descent in the backpropagation algorithm?Question 6Answera.The process of minimizing the error between the predicted output and the actual outputb.The process of adjusting the weights and biases in the forward directionc.The process of adjusting the weights and biases in the backward directiond.The process of maximizing the error between the predicted output and the actual output
Question
What is the gradient descent in the backpropagation algorithm?Question 6Answera.The process of minimizing the error between the predicted output and the actual outputb.The process of adjusting the weights and biases in the forward directionc.The process of adjusting the weights and biases in the backward directiond.The process of maximizing the error between the predicted output and the actual output
Solution
The gradient descent in the backpropagation algorithm is c. The process of adjusting the weights and biases in the backward direction.
Here's a step-by-step explanation:
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Backpropagation is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights in the network.
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The algorithm starts with the end of the network. It calculates the difference between the predicted output and the actual output. This difference is the error.
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The error is then propagated backward through the network. This means that it starts from the output layer and goes back to the input layer, adjusting the weights and biases along the way.
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The adjustments are made in such a way that the total error is minimized. This is done by subtracting a fraction of the gradient of the error with respect to each weight from the weight itself. This fraction is called the learning rate.
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The process is repeated for a number of iterations or until the network's predictions are satisfactory.
So, in essence, gradient descent in the backpropagation algorithm is the process of adjusting the weights and biases in the backward direction to minimize the error between the predicted output and the actual output.
Similar Questions
What is the bias update rule in the backpropagation algorithm?Question 18Answera.The mathematical formula that is used to update the biases based on the gradient descentb.The process of adjusting the weights and biases in the forward directionc.The process of minimizing the error between the predicted output and the actual outputd.The process of adjusting the weights and biases in the backward direction
What is the forward pass in the backpropagation algorithm?Question 12Answera.The process of adjusting the weights and biases in the backward directionb.The process of adjusting the weights and biases in the forward directionc.The process of calculating the error between the predicted output and the actual outputd.The process of predicting the output of the neural network based on the input data
What is the learning rate in backpropagation?Question 1Answera.The process of minimizing the error between the predicted output and the actual outputb.The process of adjusting the weights and biases in the backward directionc.The hyperparameter that determines the size of the weight and bias updatesd.The process of adjusting the weights and biases in the forward direction
What is the goal of the backpropagation algorithm in each iteration?Question 4Answera.To minimize the error between the predicted output and the actual outputb.To maximize the error between the predicted output and the actual output in each iterationc.To maximize the error between the predicted output and the actual outputd.To minimize the error between the predicted output and the actual output in each iteration
What is the error function in the backpropagation algorithm?Question 5Answera.The function that adjusts the weights and biases in the forward passb.The function that calculates the error between the predicted output and the actual outputc.The function that determines the activation function of the neural networkd.The function that adjusts the weights and biases in the backward pass
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