In conclusion, shortcut connections are important for overcoming the limitations posed by the vanishing gradient problem in deep neural networks. Shortcut connections are a core building block of very large models such as LLMs, and they will help facilitate more effective training by ensuring consistent gradient flow across layers when we train the GPT model in the next chapter.

总之，捷径连接对于克服深度神经网络中梯度消失问题带来的限制非常重要。快捷连接是超大型模型的核心构建块，例如LLMs，当我们在下一章训练 GPT 模型时，它们将通过确保跨层的梯度流一致来帮助促进更有效的训练。

原理是什么？

为什么梯度会消失，为什么捷径连接可以解决这个问题？