神经网络模型中隐藏层的数量
有人能够向我解释或向我指出一些资源，说明为什么（或在什么情况下）在神经网络中多个隐藏层是必要的或有用的？
Would someone be able to explain to me or point me to some resources of why (or situations where) more than one hidden layer would be necessary or useful in a neural network?
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基本上更多的层允许表示更多的功能。人工智能课程的标准书籍，Russell 和 Norvig 所著的《人工智能，一种现代方法》，在第 20 章中详细介绍了为什么多层很重要。
一个重要的一点是，通过足够大的单个隐藏层，您可以表示每个层连续函数，但至少需要 2 层才能表示每个不连续函数。
但实际上，至少 99% 的情况下单层就足够了。
Basically more layers allow more functions to be represented. The standard book for AI courses, "Artificial Intelligence, A Modern Approach" by Russell and Norvig, goes into some detail of why multiple layers matter in Chapter 20.
One important point is that with a sufficiently large single hidden layer, you can represent every continuous function, but you will need at least 2 layers to be able to represent every discontinuous function.
In practice, though, a single layer is enough at least 99% of the time.
每一层都以指数方式有效地提高了适应的潜在“复杂性”（而不是向单层添加更多节点的乘法方式）。
Each layer effectively raises the potential "complexity" of adaptation in an exponential fashion (as opposed to a multiplicative fashion of adding more nodes to a single layer).