I’m currently working on a regression model with input data of range(1, 26) and target data of range(1,  42) and I discovered that when I normalize the data to be within range(0, 1) and set learning rate to 0.001 the loss reduces by a huge factor after one epoch it’s so unbelievable than when I don’t normalize the training data (for eg: without normalization, loss can 5.0 and with normalization, loss can be 0.01 all after 1st epoch).
So this leaves me wondering could it be due to a relationship between the normalized input and learning rate or loss and learning rate tho, I don’t think the loss should change whether normalized or not coz the target data too was normalized accordingly
So what could be the cause?