Double-precision calculations are significantly slower or more expensive than single-precision calculations. For example, the NVidia Tesla which performs well on doubles is much more expensive then regular GPU. At the same time I do not know about machine learning cases where double-precision is really needed. Currently I think that 64-bit floats are needed by the model only in case then it is over-fitted. In other words doubles are needed only by bad models with bad generalization.
Are there tasks in machine learning which require double precision floating points?
machine learning;precision