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>so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful cod

by tmh88j 7y ago

>so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful code that implements nonsensical math. That seems akin to suggesting someone should learn music theory before ever attempting to pluck a guitar string. I personally learn best by putting a subject to practice and figuring out the nuances by experimenting. No one expects beginners in any subject to be capable of producing quality work, whether programming or learning to cook.

enedil 7y ago
Yes, except you learn maths at school, while you don't commonly learn music theory.
vecter 7y ago
That's a very bad analogy. People can become great musicians without ever learning music theory, since being a "musician" is such a wide category. You can be an awesome singer and not know what a diminished seventh is. I learned how to play Moonlight Sonata, 3rd Movement in high school [0] (not that that makes me good, just low-intermediate at best). I know practically nothing about music theory except how to read sheet music. On the other hand, you can never become a great, or good, or even mediocre ML engineer without understanding the math. If anything, in a production business environment, if you are responsible for creating the model yourself, you'd just be doing more harm than good. Yes, you'd be able to use pre-baked frameworks to run data through an algorithm or model that you have no idea of what it's doing or why and get some numbers and graphs out, but you won't be doing actual science or modeling. You might know how to use Python to do something as simple as run a linear regression, which if you don't know the math, you'd just have a vague understanding of it "finding the best fit line", whatever that means. However, you wouldn't understand that (under the L^2 norm) it's minimizing sum of squared errors, the properties of that (BLUE [1]), whether or not you'd want to apply regularization [2] or not, statistical tests of whether or not a linear fit is even applicable, the importance of outliers due to their overweighting under the L^2 norm, etc. If I were responsible for a modeling or prediction project, I would never trust it to a software engineer that didn't understand the math of what's actually going on. > so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful code that implements nonsensical math. I couldn't agree with the GP more. That is 100% spot on. [0] https://www.youtube.com/watch?v=zucBfXpCA6s https://www.youtube.com/watch?v=zucBfXpCA6s [1] https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem [2] https://en.wikipedia.org/wiki/Tikhonov_regularization https://en.wikipedia.org/wiki/Tikhonov_regularization
- tmh88j 7y ago
  >That's a very bad analogy. People can become great musicians without ever learning music theory, since being a "musician" is such a wide category I disagree, you're equating being good at something to being a world class expert. You can become a great python developer without ever knowing how the language implements a dictionary. You can't expect to remove the black box aspect from everything you use, that's just impossible.
  - vecter 7y ago
    We're not talking about being a great Python developer. We're talking about being a great applied ML practitioner. You simply cannot be good at that without having a firm grasp of the underlying math.
    - tmh88j 7y ago
      This was about how the content is learned, not what content is learned. >so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful code that implements nonsensical math. My response to that comment was you can learn both at the same time. I preferred to learn music theory while also learning how to play the guitar. I also preferred to learn mathematics subjects while applying them, such as machine learning.

by tmh88j 7y ago

enedil 7y ago

Yes, except you learn maths at school, while you don't commonly learn music theory.

vecter 7y ago

That's a very bad analogy. People can become great musicians without ever learning music theory, since being a "musician" is such a wide category. You can be an awesome singer and not know what a diminished seventh is. I learned how to play Moonlight Sonata, 3rd Movement in high school [0] (not that that makes me good, just low-intermediate at best). I know practically nothing about music theory except how to read sheet music. On the other hand, you can never become a great, or good, or even mediocre ML engineer without understanding the math. If anything, in a production business environment, if you are responsible for creating the model yourself, you'd just be doing more harm than good. Yes, you'd be able to use pre-baked frameworks to run data through an algorithm or model that you have no idea of what it's doing or why and get some numbers and graphs out, but you won't be doing actual science or modeling. You might know how to use Python to do something as simple as run a linear regression, which if you don't know the math, you'd just have a vague understanding of it "finding the best fit line", whatever that means. However, you wouldn't understand that (under the L^2 norm) it's minimizing sum of squared errors, the properties of that (BLUE [1]), whether or not you'd want to apply regularization [2] or not, statistical tests of whether or not a linear fit is even applicable, the importance of outliers due to their overweighting under the L^2 norm, etc. If I were responsible for a modeling or prediction project, I would never trust it to a software engineer that didn't understand the math of what's actually going on. > so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful code that implements nonsensical math. I couldn't agree with the GP more. That is 100% spot on. [0] https://www.youtube.com/watch?v=zucBfXpCA6s https://www.youtube.com/watch?v=zucBfXpCA6s [1] https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem [2] https://en.wikipedia.org/wiki/Tikhonov_regularization https://en.wikipedia.org/wiki/Tikhonov_regularization

tmh88j 7y ago
>That's a very bad analogy. People can become great musicians without ever learning music theory, since being a "musician" is such a wide category I disagree, you're equating being good at something to being a world class expert. You can become a great python developer without ever knowing how the language implements a dictionary. You can't expect to remove the black box aspect from everything you use, that's just impossible.
- vecter 7y ago
  We're not talking about being a great Python developer. We're talking about being a great applied ML practitioner. You simply cannot be good at that without having a firm grasp of the underlying math.
  - tmh88j 7y ago
    This was about how the content is learned, not what content is learned. >so anyone who benefits from this article should probably take a detour through a math book before continuing their Python ML, or else risk having beautiful code that implements nonsensical math. My response to that comment was you can learn both at the same time. I preferred to learn music theory while also learning how to play the guitar. I also preferred to learn mathematics subjects while applying them, such as machine learning.