Before the innovation of automatic feature learning approaches, a data scientist was asked to know what features to use, which model to use, how to optimize the result, and more. With the existence of huge amounts of data and high-speed devices, DL is available to automatically deduce the best features. Two of the core tasks of a data scientist are model design and optimization.
Model optimization is as important as building the model itself if not more. The previously created DL models that proved their accuracy could be reused and thus model ...
