What is the cost function used in backpropagation?Question 3Answera.The mean squared errorb.The cross-entropy lossc.The hinge lossd.The mean absolute error

The cost function used in backpropagation is typically the Mean Squared Error (MSE) or the Cross-Entropy Loss.

Here's a step-by-step explanation:

1. Backpropagation is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights of the network.

2. The loss function, also known as the cost function, is a measure of how well the neural network is performing with respect to its given training sample and the expected output.

3. The Mean Squared Error (MSE) is often used in regression problems. It calculates the square of the difference between the predicted and actual values and then averages it over the number of data points. It is given by the formula: MSE = 1/n Σ(actual - predicted)²

4. The Cross-Entropy Loss is used when the output of the neural network is a probability distribution. It is often used in classification problems. It measures the dissimilarity between the predicted probability distribution and the actual distribution.

5. The choice between MSE and Cross-Entropy Loss depends on the specific problem and the output of the neural network.

6. The other two options, Hinge Loss and Mean Absolute Error, are also types of loss functions but are less commonly used in the context of backpropagation.

So, the answer is both a. The mean squared error and b. The cross-entropy loss.