I feel I'm in a
somewhat unique position to talk about easy/hardness of machine learning; I've been working for several months on a project with a machine learning aspect with a well-cited, respected scientist in the field. But I effectively "can't do" machine learning myself. I'm a primarily 'self-trained' hacker; started programming by writing 'proggies' for AOL in middle school in like 1996.
My math starts getting pretty shaky around Calculus; vector calculus is beyond me.
I did about half the machine learning class from coursera, andrew ng's. Machine learning is conceptually much simpler than one would guess; both gradient descent and the shallow-neural network type; and in fact it is actually pretty simple to get basic things to work.
I agree with the author that the notation, etc, can be quite intimidating vs what is "really going on".
however, applied machine learning is still friggin' hard; at least to me; and I consider myself a pretty decent programmer. Naive solutions are just unusable in almost any real application, and the author's use of loops and maps are great for teaching machine learning; but everything needs to be transformed to higher level vector/matrix problems in order to be genuinely useful.
That isn't unattainable by any means; but the fact remains (imho) that without the strong base in vector calculus and idiosyncratic techniques for transforming these problems into more efficient means of computations; usable machine learning is far from "easy".
My math starts getting pretty shaky around Calculus; vector calculus is beyond me.
I did about half the machine learning class from coursera, andrew ng's. Machine learning is conceptually much simpler than one would guess; both gradient descent and the shallow-neural network type; and in fact it is actually pretty simple to get basic things to work.
I agree with the author that the notation, etc, can be quite intimidating vs what is "really going on".
however, applied machine learning is still friggin' hard; at least to me; and I consider myself a pretty decent programmer. Naive solutions are just unusable in almost any real application, and the author's use of loops and maps are great for teaching machine learning; but everything needs to be transformed to higher level vector/matrix problems in order to be genuinely useful.
That isn't unattainable by any means; but the fact remains (imho) that without the strong base in vector calculus and idiosyncratic techniques for transforming these problems into more efficient means of computations; usable machine learning is far from "easy".