Which process involves adjusting weights to minimize loss in Generative AI training?

Which process involves adjusting weights to minimize loss in Generative AI training?

• A. Backpropagation
• B. Regularization
• C. Initialization
• D. Normalization

Answer: (A)

The process that involves adjusting weights to minimize loss in Generative AI training is backpropagation. This technique is a fundamental component of training neural networks, where the model's predictions are compared to the actual outputs, and the difference (the loss) is calculated. Backpropagation works by propagating this loss backward through the network, allowing the model to compute gradients for each weight. These gradients indicate how to change the weights to reduce the loss, ultimately improving the model's performance. During the training process, backpropagation iteratively updates each weight to minimize the overall loss function, facilitating better predictions by the model. This is crucial for effective learning, as it enables the model to refine its parameters based on the data it encounters. Other processes mentioned, such as regularization, initialization, and normalization, play supportive roles in training neural networks but do not directly involve the core mechanism of adjusting weights based on loss minimization.

The process that involves adjusting weights to minimize loss in Generative AI training is backpropagation. This technique is a fundamental component of training neural networks, where the model's predictions are compared to the actual outputs, and the difference (the loss) is calculated. Backpropagation works by propagating this loss backward through the network, allowing the model to compute gradients for each weight. These gradients indicate how to change the weights to reduce the loss, ultimately improving the model's performance.

During the training process, backpropagation iteratively updates each weight to minimize the overall loss function, facilitating better predictions by the model. This is crucial for effective learning, as it enables the model to refine its parameters based on the data it encounters. Other processes mentioned, such as regularization, initialization, and normalization, play supportive roles in training neural networks but do not directly involve the core mechanism of adjusting weights based on loss minimization.