A machine learning engineer is training a deep neural network. The process involves a forward pass to generate predictions, a loss function to quantify error, and a backward pass to learn from that error. Within this training loop, what is the primary computational contribution of the backpropagation algorithm itself?
To determine the initial error value by comparing the network's final output with the ground-truth labels.
To normalize the activations of hidden layers to ensure a stable distribution of inputs during training.
To apply an optimization rule, such as momentum or Adam, to update the network's parameters.
To efficiently calculate the gradient of the loss function with respect to every weight and bias in the network.
The correct answer is that backpropagation's primary role is to efficiently compute the gradient of the loss function with respect to every parameter (weights and biases) in the network. It does this by applying the chain rule of calculus, starting from the output layer and working backward.
The option suggesting that backpropagation applies an optimization rule like Adam is incorrect. Backpropagation calculates the gradients, but the optimization algorithm (like Adam or SGD) is a separate component that uses these gradients to update the network's parameters.
The option about determining the initial error value describes the loss calculation step, which happens after the forward pass but before the backward pass and backpropagation.
The option referring to normalizing activations describes Batch Normalization, which is a separate technique used to stabilize training, not the function of backpropagation.
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