Adding a ResNet block to the end of a network makes it deeper. Which of the following is true?1 point

In the context of neural networks, adding a residual block (ResNet block) to the end of a network effectively increases the depth of the network while also enabling better training and performance. Residual connections help to mitigate problems such as vanishing gradients that can occur in deeper networks.

The true statement regarding this is that adding a ResNet block allows for the training of deeper networks without a significant increase in training error, thus improving the model's ability to learn complex features. This is because the identity shortcut connections help gradients flow more easily during backpropagation, allowing for more effective updates to weights across many layers.

Therefore, the primary benefits of adding a ResNet block include improved performance in deeper architectures and enhanced capability to learn from more complex datasets.