Ask HN: Full-on machine learning for 2020, what are the best resources?

Re:Ask HN: Full-on machine learning for 2020, what are the b... by codingslave on Tuesday December 31, 2019 @05:09PM (#21925124)

I would again argue, the natural progression for this concept would be:

1.) Trains classifier 2.) My train error was so low! Why is my validation error so high 2.) Googles -> Why is my classifier training error lower than my validation error 3.) Learns about overfitting 4.) learns about bias variance

Its always a natural progression. Reading about this stuff without encountering it means it usually doesnt stick, and really doesnt make that much sense.

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Re:Ask HN: Full-on machine learning for 2020, what are the b... by sweeneyrod on Tuesday December 31, 2019 @07:58PM (#21925885)

If you already have concepts of training and validation error then you're already there. The risk is not realising you can't test on your training data, or more subtly that you can't tune hyperparameters on your test data.

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Re:Ask HN: Full-on machine learning for 2020, what are the b... by codingslave on Tuesday December 31, 2019 @08:36PM (#21926043)

True, but I guess it depends on the person. Was just trying to give HN a view of how I write code. I've found it to be faster, but I go in knowing I will be doing a ton of googling.

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Re:Ask HN: Full-on machine learning for 2020, what are the b... by amelius on Tuesday December 31, 2019 @09:52PM (#21926333)

This is one of the very few (!) concepts you need to know to get practical with ML. Why not watch a few videos on the concepts before you begin? They are all using high-school math anyway.

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