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This is because ML mostly uses Python. And Python is an absolute clusterfuck of dependency hell. https://xkcd.com/1987/
> A great example is the machine learning: An immediate google gets you as far as 'install these 10x libs' then write this. This is because ML mostly uses Python. And Python is an absolute clusterfuck of dependency hell. https://xkcd.com/1987/
First, you should understand the math you need to be able to implement the algorithms, then you should learn the algorithms. You may "get" the code, but you will never understand it if you don't understand the mathematical objects it represents.
Simple example, linear regression, we can compute the solution without iterative improvements as in Neural Networks, why? How do we actually code that? What is the pseudo-inverse that we are computing doing there?
From here, what's the relationship of that with Newton's method for numerical optimisation? Why does alternating least squares even work?
The code will _never_ explain the underlying mathematics, it can only represent them.
I am not really sure what you are expecting for such a vast field. I recommend reading Elements of statistical learning or Bishop's book or Murphy's Probabilistic ML.
I remember attending a meetup, once, where it was supposed to be a tutorial on GraphQL. I was barely familiar with GraphQL, when I went in, and left, almost exactly the same. The tutorial was actually all about a couple of JS libraries that you could use as GraphQL abstractions. I am a Swift programmer, and was a lot more interested in the actual GraphQL interface, which was barely mentioned. Lot of that stuff, going…
You do raise a another great example with graphQL & sparQL .. I gave up trying to find any low level documentation on it because it was its so abstract from something I knew before, so I just learnt the triplets and felt comfortable to query 'enough'. I use a library now https://github.com/giacomociti/iride because I know the owner and could bug him. Other than that it was a black box.
> A great example is the machine learning: An immediate google gets you as far as 'install these 10x libs' then write this. This is because ML mostly uses Python. And Python is an absolute clusterfuck of dependency hell. https://xkcd.com/1987/
This is a symptom maybe and not a cause. I preferred R-lang for data intensive even matlab, but I don't know because all tutorials are in Python and there is no low lvl ones.
95% of programming is plumbing.
95% of programming is plumbing.
I remember attending a meetup, once, where it was supposed to be a tutorial on GraphQL. I was barely familiar with GraphQL, when I went in, and left, almost exactly the same. The tutorial was actually all about a couple of JS libraries that you could use as GraphQL abstractions. I am a Swift programmer, and was a lot more interested in the actual GraphQL interface, which was barely mentioned. Lot of that stuff, going…