Great guide - the only material I've ever read on the subject, that hasn't completely made my head hurt and my brain not grok. I'm not a maths or ML guy, just a regular programmer. I've dabbled in NN before, but only to the extent of using some libraries as a "black box" to pass parameters / training data to. No doubt there are many more people like me (too many!).
My understanding after reading this guide, is that a neural network is essentially just a formula for guessing the ideal parameter(s) of another formula, and for successively refining those parameters as more training data is passed in. I already knew this "in theory" before, but now I think most of the magic and mystery has been brushed off. Thanks karpathy!
Comments
Great guide - the only material I've ever read on the subject, that hasn't completely made my head hurt and my brain not grok. I'm not a maths or ML guy, just a regular programmer. I've dabbled in NN before, but only to the extent of using some libraries as a "black box" to pass parameters / training data to. No doubt there are many more people like me (too many!).
My understanding after reading this guide, is that a neural network is essentially just a formula for guessing the ideal parameter(s) of another formula, and for successively refining those parameters as more training data is passed in. I already knew this "in theory" before, but now I think most of the magic and mystery has been brushed off. Thanks karpathy!
I second this. I've always struggled with maths at this (relatively simple) level.
However seeing the code and then the related equation is teaching me mathematics that I couldn't grok in school 20 years ago.
This is (for me) a much better way to explain it!
Thank you for sharing.