The Secret Neural Network Formula

Choosing the right architecture for your deep learning model can drastically change the results achieved. Using too few neurons can lead to the model not finding complex relationships in the data, whereas using too many neurons can lead to an overfitting effect. With tabular data it is usually understood that not many layers are required, one or two will suffice. To help understand why this is enough look at the Universal Approximation Theorem, which proves (in simple terms) that a neural network with one layer and a finite number of neurons can approximate any continuous function. However, how do you pick the number of neurons for that neural network?