Explain the purpose of residual connections in the Transformer architecture.

The purpose of residual connections, also known as skip connections, in the Transformer architecture is to facilitate the training of very deep neural networks by addressing the vanishing gradient problem and enabling the network to learn identity mappings more easily. In a deep neural network, the gradient, which is used to update the network's weights during training, can become very small as it is backpropagated through many layers. This is the vanishing gradient problem, and it can prevent the earlier layers from learning effectively. Residual connections solve this....