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Would you suggest any books/resources to learn the theory behind these implementations so a newbie can follow along?
Pattern recognition and machine learning by Bishop is one of the canonical text books. It helps to have a linear algebra background, it includes a refresher though
One comment I have. in kNN, it is best to ensure that the neighbors list occupies O(k) space.
Would you suggest any books/resources to learn the theory behind these implementations so a newbie can follow along?
Pattern recognition and machine learning by Bishop is one of the canonical text books. It helps to have a linear algebra background, it includes a refresher though
I don't think I'll be implementing as many algorithms as you though, I should force myself to work on more projects outside my comfort zone.
A friend sent me a link to this - nice work, and I happen to be intermittently working on a very similar (and unfortunately similarly named) project - https://github.com/jarfa/ML_from_scratch/ . Check my commit history if you suspect me of copying you ;) I don't think I'll be implementing as many algorithms as you though, I should force myself to work on more projects outside my comfort zone.