Dear experts,
In this situation:
we have 60 numerical features (60 neurons in input layer).
The variation of samples are something like:
in 10 inputs: samples vary in the range of: [0, 1E-4]
in 20 inputs: samples vary in the range of: [3, 10]
in 30 inputs: samples vary in range of: [20000, 1e6]
1. How can we prepare such data set to train a Neural Network?
2. What cost function do you suggest for measuring errors and training the Neural Network?
You can start with standardizing the feature set which will convert your data to a zero mean and 1 standard deviation.
Would you please explain more? because normalization doesn't work in such data. for example if we consider 1E6 as 1, then some of our data becomes very small like 1E-10. also Logarithm seems not good.
- Badges
- Science topic