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This is dogmatism swung too far in the other direction, IMO. There are many, many successful production code bases written in dynamic languages. In my own exper

by oivey 6y ago

This is dogmatism swung too far in the other direction, IMO. There are many, many successful production code bases written in dynamic languages. In my own experience as a vision scientist/engineer, there is tremendous value in being able to quickly whip up a concept in Python and then being able to easily visualize the results. Doing this exploration in C++ is wasteful. Implementation takes much longer, the correctness brought by static typing is dubious since the code isn’t in prod, and the canned CV/visualization libraries are fewer and frequently suck in at least some way. That said, there is also tremendous value in understanding how to map your Python prototype into production code, too. Someone strong in this field can do both.

proverbialbunny 6y ago
This was addressed in the previous comment >On the quant side, *unlike on the data science side*, Vision scientist is on the data science side. You're not dealing with monetary values where floating point error compounds on itself to the point your models become garbage. Quant work is it's own unique field with its own unique prerequisites.
- oivey 6y ago
  Nothing precludes you from doing integer arithmetic in a dynamic language. I’m not a quant and this isn’t my area of expertise, but, for example, I’m pretty sure various differential equation solving methods depend on variables taking on continuous values, so floating point basically must be used. Understanding the impact of that is definitely very important. Analogously, I frequently run into numerical precision issues in image processing. Understanding how numbers are represented on a computer isn’t unique to being a quant. Understanding how the choice of representation can impact prod is also not unique to being a quant. The dynamicness of the language isn’t particularly relevant, either.
  - proverbialbunny 6y ago
    >Nothing precludes you from doing integer arithmetic in a dynamic language. You would be surprised. The second you use pandas with a custom data type (let alone any other library you'd want to use) it can randomly auto convert it to a float. Furthermore identifying when it randomly converts the type on you is a pain. >so floating point basically must be used. Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error.
    - hntrader 6y ago
      Quants don't care about floating point precision in research. It's just applied stats
      - proverbialbunny 6y ago
        I do, because the results from my research varies when I'm validating the model.
    - dragonwriter 6y ago
      > The second you use pandas with a custom data type That's a pandas (and maybe numpy) issue, not a dynamic language issue. (If you want to generalize from the specific libraries more accurately than “dynamic language”, it's “using a low-level library whose type system doesn't match the host language type system” issue. > Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error. No, a type that is like binary floating point in every way except base 10 instead of base 2 would be decimal floating point, not fixed point. Decimal fixed point is different from binary floating point in more ways than base.

by oivey 6y ago

proverbialbunny 6y ago

This was addressed in the previous comment >On the quant side, *unlike on the data science side*, Vision scientist is on the data science side. You're not dealing with monetary values where floating point error compounds on itself to the point your models become garbage. Quant work is it's own unique field with its own unique prerequisites.

oivey 6y ago
Nothing precludes you from doing integer arithmetic in a dynamic language. I’m not a quant and this isn’t my area of expertise, but, for example, I’m pretty sure various differential equation solving methods depend on variables taking on continuous values, so floating point basically must be used. Understanding the impact of that is definitely very important. Analogously, I frequently run into numerical precision issues in image processing. Understanding how numbers are represented on a computer isn’t unique to being a quant. Understanding how the choice of representation can impact prod is also not unique to being a quant. The dynamicness of the language isn’t particularly relevant, either.
- proverbialbunny 6y ago
  >Nothing precludes you from doing integer arithmetic in a dynamic language. You would be surprised. The second you use pandas with a custom data type (let alone any other library you'd want to use) it can randomly auto convert it to a float. Furthermore identifying when it randomly converts the type on you is a pain. >so floating point basically must be used. Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error.
  - hntrader 6y ago
    Quants don't care about floating point precision in research. It's just applied stats
    - proverbialbunny 6y ago
      I do, because the results from my research varies when I'm validating the model.
  - dragonwriter 6y ago
    > The second you use pandas with a custom data type That's a pandas (and maybe numpy) issue, not a dynamic language issue. (If you want to generalize from the specific libraries more accurately than “dynamic language”, it's “using a low-level library whose type system doesn't match the host language type system” issue. > Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error. No, a type that is like binary floating point in every way except base 10 instead of base 2 would be decimal floating point, not fixed point. Decimal fixed point is different from binary floating point in more ways than base.