So say I want the user to type the first 5 letters of a word he or she is thinking about; I want the neural network to output the remaining letters in the word.
However, should I:
- Convert the 5-letter string to binary (
- This means that I will have
5 * 8 = 40input neurons
Or should I:
- Link letters to numbers (
- Divide this number by
- Resulting in
- Meaning I will only have 5x1 input neurons
The same would count for the output of course!
However, what is more effective? Which normalization will have a more positive effect on the neural-network training (through backpropagation and genetic algorithms, both). And are there any papers on this?