4 ms·

Naive question : is this strategy equally effective for all ML models?

• kgwgk 9y ago
  This strategy is equally effective for most things in life.
  • doktrin 9y ago
    > This strategy is equally effective for most things in life. Not if we understand 'effective' to also mean 'cost & time effective' You'd learn a lot by building a nuclear reactor from first principles, but it's not the most effective way to develop an intuition about how one operates.
    • yorwba 9y ago
      I think you want to talk about whether the strategy is efficient, which I agree it is not. However, if you already tried understanding several general descriptions and it didn't work out, implementing something from scratch is an inefficient but effective way of really grokking it.
      • doktrin 9y ago
        > I think you want to talk about whether the strategy is efficient effective and efficient are synonyms in this context http://www.thesaurus.com/browse/efficient?s=t http://www.thesaurus.com/browse/efficient?s=t http://www.dictionary.com/browse/efficient?s=t http://www.dictionary.com/browse/efficient?s=t http://www.dictionary.com/browse/effective?s=t http://www.dictionary.com/browse/effective?s=t
• jclos 9y ago
  Some require a lot more understanding than others (for instance I'm not sure I'd be comfortable implementing a kernelized SVM from scratch, even though intuitively I know how it works) but basic neural networks (simple perceptron, simple feedforward network, simple recurrent network) are quite easy to grasp, and backpropagation is very intuitive. You can even use finite difference approximation [1] to bypass the derivatives when you're starting (at the cost of some efficiency) and figure out the rest as you go. [1] https://en.wikipedia.org/wiki/Finite_difference https://en.wikipedia.org/wiki/Finite_difference