This question is broad, and the normalisation strategy is going to depend on the nature of your indicator.
Assuming the technical indicators are a time series, then two simple approaches for normalising your data are:
- Calculate the difference between each time step. If you are feeding multiple signals into a neural network you should confirm the values are of similar magnitude, and scale if necessary (obviously your features will not be neatly bounded [0,1] unless that's the nature of the indicator). An easy to understand Python tutorial using this strategy can be found in the Microsoft Cognitive Toolkit; or
- Calculate the returns / percentage change of the indicator at each time step which will give you a scaled result between 0 and 1. i.e.
(t2 - t1) / t1 where
t1 is the signal at time step 1 and
t2 is the signal at time step 2.
Machine Learning Mastery is a good resource for tutorials that will include appropriate normalisation steps for different sorts of features with a strong focus on time series data, and to a degree for different deep learning models.
Finally, if you are up to the challenge, review the chapter Fractionally Differentiated Features in Advances in Financial Machine Learning by Marcos Lopez De Prado. Excellent thinking and review of the challenges of dealing with the kinds of features you want to use.