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This is well-written and I applaud any step toward demystifying the sometimes scary sounding concepts that drive much of the ML algorithms. Knowing you can pre
by nkozyra 10y ago
This is well-written and I applaud any step toward demystifying the sometimes scary sounding concepts that drive much of the ML algorithms.
Knowing you can pretty quickly whip up a KNN or ANN in a few hundred lines of code or fewer is one of the more eye-opening parts of the delving in. For the most part, supervised learning follows a pretty reliable path and each algorithm obviously varies in approach, but I know I originally thought "deep learning? ugh, sounds abstract and complicated" before realizing it was all just a deep ANN.
Long story short: dig in. It's unlikely to be as complex as you think. And if you've ever had an algorithms class (or worked as a professional software dev) none of it should be too daunting. Your only problem will be keeping up the charade if people around you think ML/AI is some sort of magic.
- enraged_camel 10y agoExcuse my ignorance but what do KNN/ANN stand for?
- dev1n 10y agok-nearest neighbor vs artificial neural network.
- qiemem 10y agok-nearest neighbors (the k is just the number of nearby data points you consider when classifying a point), a clustering algorithm, and artificial neutral network
- nkozyra 10y agoAs mentioned, k-nearest neighbor, a x-dimensional similarity search algorithm (you can write this in python in like 25 lines, honest to god). ANN, artificial neural network, which is what things like tensorflow were borne. Neural networks were kind of passe before they were revived by "deep learning"; their essentially a self-correcting, self-referencing set of logic gates.
- arrmn 10y agoI was also thinking ML is a dark art and only a few selected ones are able to understand it, but then I started to look more into it took the coursera ML course and now I'm working trough some other lectures and it's easier than I imagined. Like you said, you just need to start working with it.