Currently, I designed a neural network with one hidden layer, with cross entropy cost function and softmax activation function to predict the outcome of a tennis game.
The input is a matrix, where each row is a training data, each column is a variable.
These features are having very different units. For numerical, we have ages, weight, height, international ranking, historical head-to-head winning ratio.
We have indicator variable: Injury
We have categorical variable (one hot encoding): Fatigue, nationalities.
I haven't tried to feed the neural my input data yet. I'm still processing my data.
Now, it comes to me that my input data for each training case having very different variables; I'm wondering what is a good way of standardizing this input?