The vanishing gradient problem primarily affects the learning capabilities of shallow networks more than deep networks.

The statement is false: the vanishing gradient problem hurts deep networks far more than shallow ones. When back‑propagation moves through many layers, each weight multiplies the gradient by a factor usually less than one, so after dozens of layers the gradient can become almost zero, preventing learning in those early layers. Shallow networks have only a few multiplications, so the gradient remains large enough for useful updates. For example, in a 10‑layer ReLU network the gradient can shrink to 10⁻⁶, while in a 3‑layer network it stays close to the original value, allowing training to proceed. Thus, deep networks are the main victims of vanishing gradients.