I generate 100000 sample at point 1,2,3,4 and 5 so total there are 5 lac samples. I have to classify data into 2 classes. I want to normalize the data in the range of zero to one. If I normalize a single feature in all the 1 lac samples at one point such that first column or the first feature in all 1 lac samples of point 1 $${A}_1,_1 ,{ A}_2,_1 + { A}_3,_1 +....{ A}_N,_1$$ values lies from zero to one. And i repeat the same procedure for all the features. Where as in one sample there are total 16 features. But after such normalization will I not meet vanishing gradient problem? Because after this normalization first sample of point 1 contains features such that [0,0,0,0 ....0]. So during training when machine met with this sample all weights will be zero. I have doubt on my normalization method.