What is the primary purpose of the backpropagation algorithm in training a neural network?ATo compute the output of the networkBTo initialize the weights of the networkCTo update the weights of the network by minimizing the loss functionDTo determine the optimal network architecture

The primary purpose of the backpropagation algorithm in training a neural network is C: To update the weights of the network by minimizing the loss function.

Here's a step-by-step explanation:

1. Initialize the weights: Before the learning process begins, the weights of the neural network are typically initialized with small random values.

2. Feedforward: Each input unit sends its value to all units it is connected to, multiplied by the corresponding weight. This process continues layer by layer until it reaches the output layer, producing the network's output.

3. Compute the error: The network's output is compared to the desired output, and the difference is calculated using a loss function.

4. Backpropagate the error: The error is then propagated backwards through the network, starting from the output layer and moving towards the input layer. This is done by taking the derivative of the loss function with respect to the weights (also known as the gradient), which indicates how much a small change in the weights would change the output.

5. Update the weights: The weights are then updated in the opposite direction of the gradient. This is done to minimize the loss function, i.e., to make the network's output closer to the desired output.

6. Repeat steps 2-5: This process is repeated for many iterations (or 'epochs') until the network's performance is satisfactory.