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

The correct answer is: A. 1, 2, 3, 4, 5

Gradient descent is an iterative optimization algorithm for finding the minimum of a function. It is used in machine learning to train neural networks. The steps involved in gradient descent are as follows:

1. Initialize the weights and biases of the network randomly.
2. Pass an input through the network and get values from the output layer.
3. Calculate the error between the actual value and the predicted value.
4. Go to each neuron which contributes to the error and change its respective values to reduce the error.
5. Repeat steps 2-4 until the error is minimized.

The following is a brief explanation of each step:

1. Initialize the weights and biases of the network randomly. This is done to ensure that the network starts from a random point in the search space.
2. Pass an input through the network and get values from the output layer. This is done to calculate the error between the actual value and the predicted value.
3. Calculate the error between the actual value and the predicted value. This is done to determine how much the network needs to be updated.
4. Go to each neuron which contributes to the error and change its respective values to reduce the error. This is done by updating the weights and biases of the network.
5. Repeat steps 2-4 until the error is minimized. This is done until the network converges to a minimum value for the error.