AI News, 5 Mistakes Programmers Make when Starting in Machine Learning

5 Mistakes Programmers Make when Starting in Machine Learning

mindset shift is required to be effective at machine learning, from technology to process, from precision to “good enough”, but the same could be said for other complex methods that programmers are interested in adopting.

Starting in machine learning by writing code can make things difficult because it means that you are solving at least two problems rather than one: how a technique works so that you can implement it and how to apply the technique to a given problem.

It is much easier to work on one problem at a time and leverage machine learning and statistical environments and libraries of algorithms to learn how to apply a technique to a problem.

This allows you to spot check and tune a variety of algorithms relatively quickly and tune the one or two that look promising rather than investing large amounts of time interpreting ambiguous research papers containing algorithm descriptions.

There is great advantage in scripting data preparation, algorithm testing and tuning and the preparation of results in order to gain the benefits of rigor and speed of improvement.

The failure to start with automation (such as Makefiles or similar build system) is likely due to the fact that many programmers come to machine learning from books and courses that have less focus on the applied nature of the field.

Hundreds and thousands of people have likely implemented the algorithm you are implementing before you or have solved a problem type similar to the problem you are solving, exploit their lessons learned.

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