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You're overstating things a bit. There has never been a "need to learn multiple frameworks in multiple languages." As a beginner, you pick one and go with that

You're overstating things a bit. There has never been a "need to learn multiple frameworks in multiple languages." As a beginner, you pick one and go with that (as evidenced by the fact that... you've picked one!). This announcement doesn't change that situation. Nobody needs to use multiple frameworks unless you're someone (not a beginner) who wants to be able to take the code from research papers or something.

tedmiston 11y ago
Researchers need multiple frameworks because the feature sets aren't the same in each. I haven't done anything with deep learning, but I worked in a research lab with others who did. On the image processing side, we prototyped code in OpenCV, both with Python and C++, and MATLAB (with toolboxes) regularly because of this. At the end of the day they have a limited amount of time and just want to test their idea the fastest way they can.
- argonaut 11y ago
  I don't think this is the reason. You can do pretty much anything in Torch/Theano. But, researchers are lazy and writing code is only a means to an end. They will shamelessly copy/hack as much open source code as possible, so if a framework has some quick module for a desired algorithm, or if a researcher wrote their paper in some framework, and you want to build off that paper, then you'll just copy that.
  - tedmiston 11y ago
    That's definitely correct. One related unsolved problem in that type of research is getting people to share their actual code. Especially when multiple universities are at the bleeding edge in a field, they often publish just enough [1] to prove their point without giving everyone else the same foundation to build on easily. Even in science it gets political... who knew. 1: i.e., just their algorithms, or their code without very useful implementation-level optimizations
    - argonaut 11y ago
      In ML, you can generally email the authors and very often they will be willing to send you (their really crappy) code. Although it probably helped that I sent these emails from my academic email address.
      - sgt101 11y ago
        I guess that by the time the paper is in the public domain things have moved on - it's six months from submission to publication.
      - pacala 11y ago
        > There has never been a "need to learn multiple frameworks in multiple languages." > you can generally email the authors and very often they will be willing to send you (their really crappy) code. Code obtained from multiple authors, or even from the same author but different time periods, is code written using multiple frameworks in multiple languages. Standardizing on Python / TensorFlow reduces the risk of cognitive load along one's journey and is likely to speed up the field. If speed is what the field was missing :)
aab0 11y ago
> Nobody needs to use multiple frameworks unless you're a researcher or something who wants to be able to take use the code from other people's papers or something. I never said I wasn't. I'm interested in more than one thing, and so it makes me sad to see that the cutting-edge stuff I'm interested in is implemented in a variety of languages and frameworks (typically for no better reason than that particular researcher uses that particular framework), which means I either need to invest a huge amount of time and effort into becoming a polyglot or abandon any more in depth understanding than the paper.
- argonaut 11y ago
  I agree with you; your original comment was talking about beginners specifically, though. That being said, it's the natural state of any field moving fast. Web development has even worse fragmentation. Mobile development has about the same amount of fragmentation. There are a dozen distributed computing frameworks (Hadoop? A dozen things on top of Hadoop? Spark? Mesos? Kubernetes?).
IshKebab 11y ago
I agree. The frameworks are similar enough that you don't really get much from switching between them. There are differences, sure, but they are far smaller than the similarities.
deleted 11y ago
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tedmiston 11y ago

Researchers need multiple frameworks because the feature sets aren't the same in each. I haven't done anything with deep learning, but I worked in a research lab with others who did. On the image processing side, we prototyped code in OpenCV, both with Python and C++, and MATLAB (with toolboxes) regularly because of this. At the end of the day they have a limited amount of time and just want to test their idea the fastest way they can.

argonaut 11y ago
I don't think this is the reason. You can do pretty much anything in Torch/Theano. But, researchers are lazy and writing code is only a means to an end. They will shamelessly copy/hack as much open source code as possible, so if a framework has some quick module for a desired algorithm, or if a researcher wrote their paper in some framework, and you want to build off that paper, then you'll just copy that.
- tedmiston 11y ago
  That's definitely correct. One related unsolved problem in that type of research is getting people to share their actual code. Especially when multiple universities are at the bleeding edge in a field, they often publish just enough [1] to prove their point without giving everyone else the same foundation to build on easily. Even in science it gets political... who knew. 1: i.e., just their algorithms, or their code without very useful implementation-level optimizations
  - argonaut 11y ago
    In ML, you can generally email the authors and very often they will be willing to send you (their really crappy) code. Although it probably helped that I sent these emails from my academic email address.
    - sgt101 11y ago
      I guess that by the time the paper is in the public domain things have moved on - it's six months from submission to publication.
    - pacala 11y ago
      > There has never been a "need to learn multiple frameworks in multiple languages." > you can generally email the authors and very often they will be willing to send you (their really crappy) code. Code obtained from multiple authors, or even from the same author but different time periods, is code written using multiple frameworks in multiple languages. Standardizing on Python / TensorFlow reduces the risk of cognitive load along one's journey and is likely to speed up the field. If speed is what the field was missing :)

aab0 11y ago

> Nobody needs to use multiple frameworks unless you're a researcher or something who wants to be able to take use the code from other people's papers or something. I never said I wasn't. I'm interested in more than one thing, and so it makes me sad to see that the cutting-edge stuff I'm interested in is implemented in a variety of languages and frameworks (typically for no better reason than that particular researcher uses that particular framework), which means I either need to invest a huge amount of time and effort into becoming a polyglot or abandon any more in depth understanding than the paper.

argonaut 11y ago
I agree with you; your original comment was talking about beginners specifically, though. That being said, it's the natural state of any field moving fast. Web development has even worse fragmentation. Mobile development has about the same amount of fragmentation. There are a dozen distributed computing frameworks (Hadoop? A dozen things on top of Hadoop? Spark? Mesos? Kubernetes?).

IshKebab 11y ago

I agree. The frameworks are similar enough that you don't really get much from switching between them. There are differences, sure, but they are far smaller than the similarities.

deleted 11y ago

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