This article is the third part of a mini-series on structuring and executing machine learning projects with a core focus on deep learning. (The earlier two articles are How to plan and execute your ML and DL projects and Becoming One With the Data.) This article’s aim is to discuss several aspects of training neural networks in a methodical way in order to minimize overfitting and develop a checklist of the steps that make that possible.

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