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I actually think the hardest part about ML is the lingo. It's very alienating that even simple concepts seem to have their own lingo. A lot of the ideas are j

by bhntr3 10y ago

I actually think the hardest part about ML is the lingo. It's very alienating that even simple concepts seem to have their own lingo. A lot of the ideas are just what you as a developer might do intuitively if you had to implement it. But the language tends to be a bit mathy and obscure. So, when you try to read something without understanding the lingo, it seems impenetrable. But once you know things like "quantization is basically rounding" . . . it becomes easier. Since ML comes from statistics, math, programming, but also other scientific fields, it can even have many terms for essentially the same thing. For me, as a developer, it was actually easiest to just read some tutorials like the docs for scikit learn and then just start digging through the code of a bunch of libraries. How people name the classes tells you what they think things should be called. But the code tells you what it actually does. I just bounced back and forth between code, tutorials/blogs and books. After a few months, I can actually have a reasonable conversation with our ML people in the language they use and everything else I look at seems easier because I understand most of the terms. I think asking how to learn ML is a lot like asking how to learn German. It might feel like you need to start with the grammar rules. But I think immersion is the best way. Get the vocabulary, then come back to the rules. I also find that having a burning question in my mind helps me with immersion. So, if you can find a project that drives you, maybe that will help. So starting with the math fundamentals as a developer seems like an easy way to burn yourself out. But everyone does learn differently. If not, there wouldn't be so many ML algorithms, right? Right?

collyw 10y ago
I agree. I started Andrew NG's coursera course and it seemed pretty maths heavy and dry. I started reading through the tensorflow tutorial and couple other more hands on approaches. I get a better idea of what is actually happening in the second. Am I likely to need matrix multiplication if I start doing machine learning, or that the equivalent of writing a sort algorithm for a web dev - maybe useful to know the concepts, but in reality you won't actually use it?
- bhntr3 10y ago
  My experience so far is that it's important to know WHY you use linear algebra. The idea to me is that almost all input data passed to a machine learning algorithm should be transformed to a multi-dimensional array of doubles. It's easier to write algorithms against this. Since a lot of ML libraries use native libraries for linear algebra, you might see a lot of implementations that are written in terms of linear algebra operations. So, if you're trying to read the code and you don't understand what the operations do, it may be hard to grok. So, yeah, I think some understanding of linear algebra is necessary. Because it's sort of the atomic set of operations underlying most ML you'll see. To read the code, you need to be able to read the linear algebra. But you probably don't need to go read a book on linear algebra. I tried that and it pulled me away from what I wanted to know. It might be enough to just understand the numpy docs.

by bhntr3 10y ago

collyw 10y ago

I agree. I started Andrew NG's coursera course and it seemed pretty maths heavy and dry. I started reading through the tensorflow tutorial and couple other more hands on approaches. I get a better idea of what is actually happening in the second. Am I likely to need matrix multiplication if I start doing machine learning, or that the equivalent of writing a sort algorithm for a web dev - maybe useful to know the concepts, but in reality you won't actually use it?

bhntr3 10y ago
My experience so far is that it's important to know WHY you use linear algebra. The idea to me is that almost all input data passed to a machine learning algorithm should be transformed to a multi-dimensional array of doubles. It's easier to write algorithms against this. Since a lot of ML libraries use native libraries for linear algebra, you might see a lot of implementations that are written in terms of linear algebra operations. So, if you're trying to read the code and you don't understand what the operations do, it may be hard to grok. So, yeah, I think some understanding of linear algebra is necessary. Because it's sort of the atomic set of operations underlying most ML you'll see. To read the code, you need to be able to read the linear algebra. But you probably don't need to go read a book on linear algebra. I tried that and it pulled me away from what I wanted to know. It might be enough to just understand the numpy docs.