What is a characteristic of training using backpropagation?

Training using backpropagation is fundamentally characterized by comparing the initial and desired outputs to identify and correct errors. This process is essential in supervised learning, where the aim is to minimize the difference between the predicted output of the network and the actual desired output.

In backpropagation, after a forward pass through the network, the output is compared to the target value, and an error signal is computed. This error is then propagated back through the network to update the weights, enabling the model to learn from its mistakes. The iterative nature of this process—where weights are continually adjusted based on the error—allows the network to gradually improve its prediction accuracy over time.

The other options do not accurately reflect the characteristics of backpropagation. Initial weights must be adjusted for the model to learn effectively. The method involves understanding the relationships between inputs and outputs rather than solely decoding them. Lastly, backpropagation inherently involves an iterative process as it repeatedly adjusts weights based on error calculations. Thus, the focus on comparing outputs for error correction is what fundamentally distinguishes backpropagation in training neural networks.