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Do you need feature engineering?

Do you need feature engineering?
By Santiago • Issue #30 • View online
This week I started asking questions about Web 3.0.
(There’s nothing on this newsletter about Web 3.0, but I think it’s something that everyone should at least understand, and that’s why I bring it up.)
Unfortunately, my questions led to more questions, and I left with a single clear answer: we are still figuring things out. It’s very early, and the hype around Web 3.0 is mainly supported by optimism and wishful thinking.
This is not a bad thing, but it’s a turnoff for many people. Excessive hype has never been a welcome precursor of anything that matters. We’ll need to wait and see where this goes.
My advice is to get involved. Read about this. Ask questions. Progress is inevitable, so better use our voices to shape it.

Do you need feature engineering?
Neural networks are powerful. Deep networks even more.
Wouldn’t these networks solve the problem of feature engineering? Aren’t they capable of doing this for us?
I've heard multiple times that you don't need to do any feature engineering or selection whenever you are using neural networks.

This is not true.

Yes, neural networks can extract patterns and ignore unnecessary features from the dataset, but this is usually not enough.

What comes first?
Should you learn the theory before jumping on practical problems, or can you start making things and fill in the blanks later?
Which approach is the right one for you?
"You can't use an algorithm unless you understand how it works."

That's what many people say. But I don't believe it.

This is how you can build expertise: ↓
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