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Which Model and How Much Data?

Recipes for Answering The Two Questions That Drive Data Scientist Crazy. Building deep learning applications in the real world is a never-ending process of selecting and refining the right elements of a specific solution.

Among those elements, the selection of the correct model and the right structure of the training dataset are, arguably, the two most important decisions that data scientists need to make when architecting deep learning solutions. How to decide what deep learning model to use for a specific problem? How do we know whether we are using the correct training dataset or we should gather more data?

Source: towardsdatascience.com
Author: Jesus Rodriguez

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