Which process involves forward pass, loss function, backpropagation, optimization, and continues until the loss value is negligible?

Which process involves forward pass, loss function, backpropagation, optimization, and continues until the loss value is negligible?

• A. Training Loop
• B. Generator in GAN
• C. Prompt Engineering
• D. Discriminator in GAN

Answer: (A)

The process described is the training loop. In this loop, you feed data through the model to get predictions (forward pass), measure how far those predictions are from the true values using a loss function, compute how to adjust the model’s parameters by propagating that error backward through the network (backpropagation), and then update the parameters with an optimizer to reduce the loss. This sequence is repeated across many batches and epochs, continuing until the loss is negligible or another stopping criterion is met (such as reaching a maximum number of iterations or achieving satisfactory performance). Note that while GANs involve training a Generator and a Discriminator using the same basic steps, the overarching process remains the same: forward pass, loss, backpropagation, and optimization within a looping training procedure. Prompt engineering, on the other hand, is about crafting inputs rather than training the model.

The process described is the training loop. In this loop, you feed data through the model to get predictions (forward pass), measure how far those predictions are from the true values using a loss function, compute how to adjust the model’s parameters by propagating that error backward through the network (backpropagation), and then update the parameters with an optimizer to reduce the loss. This sequence is repeated across many batches and epochs, continuing until the loss is negligible or another stopping criterion is met (such as reaching a maximum number of iterations or achieving satisfactory performance).

Note that while GANs involve training a Generator and a Discriminator using the same basic steps, the overarching process remains the same: forward pass, loss, backpropagation, and optimization within a looping training procedure. Prompt engineering, on the other hand, is about crafting inputs rather than training the model.