In the context of deep learning architectures, how can proper weight initialization and gradient clipping address the vanishing/exploding gradients problem effectively?

When training deep neural networks, signals can shrink to near zero or grow uncontrollably as they propagate through many layers, which is the vanishing or exploding gradient problem. Proper weight initialization, such as Xavier or He schemes, sets initial weights so that the variance of activations stays roughly constant across layers, keeping gradients at a usable scale from the start. Gradient clipping limits the maximum norm of the back‑propagated gradient, preventing any single update from becoming too large and destabilizing learning. Together, a balanced initialization keeps gradients in a healthy range while clipping stops runaway growth during training, allowing the network to learn effectively. For example, initializing a 50‑layer LSTM with He initialization and clipping gradients to a norm of 5 lets the model learn long‑term dependencies without the loss exploding or vanishing.