I dont think you need to know (or at least should not need to know) much stats at all to use pre-built libraries like TensorFlow.
It feels to me that a lot of the ML courses around concentrate almost entirely on the stats & maths side of ML though. This strikes me as a bit of mental-masturbation.
To teach people how to program from zero-knowledge, we don't first teach them how modern compilers or the JVM works and how they do their complex optimisations and JIT etc. Why are we teaching people how to use ML from zero-knowledge the absolute raw nuts and bolts of the maths involved (complete with all of the mathematical proofs to prove that something works etc)?
Sure eventually it would be useful to know what is going on with the maths, just like with programming it eventually can be useful to know what the compiler/JVM is really doing, but a LOT of productive stuff can be done when blissfully ignorant of what TensorFlow/the JVM is doing.
ML is easy, but the courses are often too aloof and strike me as academically focused on the maths purely for the sake of the maths itself, rather than on what ML can do. ML is not hard - any programmer can understand it, but the maths is off putting to programmers who are not mathematicians (the majority I'd say)
I dont think you need to know (or at least should not need to know) much stats at all to use pre-built libraries like TensorFlow.
It feels to me that a lot of the ML courses around concentrate almost entirely on the stats & maths side of ML though. This strikes me as a bit of mental-masturbation.
To teach people how to program from zero-knowledge, we don't first teach them how modern compilers or the JVM works and how they do their complex optimisations and JIT etc. Why are we teaching people how to use ML from zero-knowledge the absolute raw nuts and bolts of the maths involved (complete with all of the mathematical proofs to prove that something works etc)?
Sure eventually it would be useful to know what is going on with the maths, just like with programming it eventually can be useful to know what the compiler/JVM is really doing, but a LOT of productive stuff can be done when blissfully ignorant of what TensorFlow/the JVM is doing.
ML is easy, but the courses are often too aloof and strike me as academically focused on the maths purely for the sake of the maths itself, rather than on what ML can do. ML is not hard - any programmer can understand it, but the maths is off putting to programmers who are not mathematicians (the majority I'd say)