What is the difference between float64 and double in TensorFlow? In storing floating point values both overflow and underflow problems cause loss of data. In machine learning tasks underflow is a common problem. I wanted to know if double is better than float64 in TensorFlow or not and if there is any difference between them?
Taking a look at tensorflow's dtypes.py, there's this line:

double = float64


So double is exactly the same at float64.