Sensible methods to speed up heavy workloads with GPU optimization in Python
One of many largest challenges that knowledge scientists face is the prolonged runtime of Python code when dealing with extraordinarily massive datasets or extremely complicated machine studying/deep studying fashions. Many strategies have confirmed efficient for enhancing code effectivity, resembling dimensionality discount, mannequin optimization, and have choice — these are algorithm-based options. An alternative choice to handle this problem is to make use of a special programming language in sure circumstances. In right now’s article, I received’t give attention to algorithm-based strategies for enhancing code effectivity. As an alternative, I’ll talk about sensible methods which are each handy and straightforward to grasp.
For instance, I’ll use the On-line Retail dataset, a publicly accessible dataset below a Artistic Commons Attribution 4.0 Worldwide (CC BY 4.0) license. You may obtain the unique dataset On-line Retail knowledge from the UCI Machine Studying Repository. This dataset incorporates all of the transactional knowledge occurring between a selected interval for a UK-based and registered non-store on-line retail. The goal is to coach a mannequin to foretell whether or not the client would make a repurchase and the next python code is used to attain the target.
