Deep learning model architecture
Hello everyone, I'm starting in deep learning and I would like to apply a ternary classification model to text data, the representation of an indivdual data is as follows:
- Indiv = [V_1,V_2,...,V_300]
- Vi = [(T_i,S_i,date_i)]
when T_i is a matrix of size (256,5), S_i is a matrix of size (256,5) and date_i is converted to a value between 0 and 1.
Do I have to do pre-processing in order to reduce the size of the data if yes what tasks do you propose, and what model architecture do you advise me to apply?
Thank you in advance !!!