[dblohm7]

As somebody whose machine learning expertise consists of the first cohort of Andrew Ng's MOOC back in 2011, I'm not too surprised. One of the big takeaways I took from that experience was the importance of getting the features right.

[Animats]

I remember that class. Someone from Blackrock taught it at Hacker Dojo. The good old days of support vector machines and Matlab.

[ismailmaj]

This was very important with classical machine learning, now with deep learning, feature engineering became useless as the model can learn the relevant features by itself.

However, having a quality and diverse dataset is more important now than ever.

[Salgat]

That depends on the type of data, and regardless, your goal is to minimizing the input data since it has a direct impact on performance overhead and duration of inference.

[beckhamc]

no we just replaced feature engineering with architectural engineering

[geoduck14]

>was the importance of getting the features right.

Yeah, but also knowing which features to get right. Right?