“Small” neural network (fewer parameters; more prone to underfitting)
Computationally cheaper
"Large" neural network (more parameters; more prone to overfitting)
Computationally more expensive.
Use regularization (λ) to address overfitting.
简单的神经网络（更少的参数）容易出现欠拟合，但优点是计算简单。
复杂的神经网络（跟多参数，更复杂的结构）一般情况下意味着更好的性能，但是计算成本高，而且容易出现过拟合现象，这时需要运用正则化解决过拟合问题。
原文：https://www.cnblogs.com/qkloveslife/p/9887119.html