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Deep learning has provided the world of data science with highly effective tools that can address problems in virtually any domain, and using nearly any kind of data. However, the non-intuitive features deduced and used by deep learning algorithms require a very careful experimental design, and a failure to meet that requirement can lead to miserably flawed results, regardless of the quality of the data or the structure of the deep learning network.
I first noticed such flaws almost…Continue
While the skills required or expected from data scientists can vary based on the organization or domain they work in, being a data scientist can be viewed not merely as owning a set of skills, but also as having a certain mindset. In that sense I can differentiate between passive data scientists and active data scientists.
The passive data scientists will use the data they receive, or perform basic tasks to collect data stored in one or several central sources. Once they have the…Continue