🌱 Introduction

πŸ‘·β€β™€οΈ Guidelines for Structuring Machine Learning Projects

πŸ‘©β€πŸŽ“ Orthogonalisation

One of the challenges with building machine learning systems is that there are so many things we could try. Including, for example, so many hyperparameters we could tune. The art of knowing what parameter to tune to get what effect, is called orthogonalisation.

What should we pay attention to while evaluating an ML project? How to optimize it? How to speed up? Since there are a lot of parameters how to know where to fix and which parameter to tune? πŸ€”πŸ€•

Before answering these questions let's take a look at the whole process 🧐

β›“ Chain of assumptions in ML

The model should:

Fit training set well on cost function (Human level performance ❌❌)

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Fit dev set well on cost function

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Fit test set well on cost function

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Perform well in real world ✨

Figuring out what is exactly wrong can help us to choose a suitable solution and then to fix that part without affecting the whole project πŸ‘©β€πŸ”§