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>Java for enterprise or Android >C# if you are windows developer Picking Java over kotlin for android, or lumping C# into "just for windows" bucket is exactly

by Xeamek 4y ago

>Java for enterprise or Android >C# if you are windows developer Picking Java over kotlin for android, or lumping C# into "just for windows" bucket is exactly the ignorance that leads you to "we don't have choices" conclusion. If you always default to literally top1 language for given domain, then how can you expect to have multiple choices? I'm not talking about using some brand new gimmick that came out yesterday and will die tomorrow, but if you aren't able to leave your comfort zone even just-so slightly to update your stereotypes about given given domain and its trends, then by definition you won't see the alternatives.

synergy20 4y ago
same can be said to typescript, did not mention them to make the point.
f1shy 4y ago
TBH I was shocked by "Python for machine learning". I do not know where that comes from. Maybe because some important toolkits are available for Neural Networks? But machine learning is not neural networks... you can do it with NNs, but there are so many other algorithms... I'm really surprise of this kind of posts in hacker news. I thought the audience here has other experiences.
- plonk 4y ago
  Python is the main language for PyTorch and TensorFlow, there are few ML frameworks more complete than Scikit-learn, I'm not aware of an equivalent to NumPy in other languages, and this list is probably missing other things I don't use. What's the alternative for quick ML prototyping?
  - didibus 4y ago
    R, Matlab, Julia are some common alternatives. Java/Scala I've also seen, specifically things around Spark ML, and Spark compute. That said, Python is definitely the most common nowadays I'd say.
- tinco 4y ago
  That's literally the only one that didn't shock me. What universe do you live in where machine learning algorithms aren't tied together with Python? And what industry where machine learning is not mainly neural networks?
  - f1shy 4y ago
    In the company I work most of it is done in matlab for "putting together" then goes to C++. Again not neural networks. I'm saying all the other machine learning. Maybe rare case.
    - p1esk 4y ago
      What kind of machine learning does your company do? It's probably something simple, because for anything complicated, like computer vision, speech recognition, any non-trivial NLP, and large scale recommender systems you do need neural networks.
      - f1shy 4y ago
        Autonomous driving, including, for example, computer vision. No NLP or speech. About the "need" or neural networks, there is a lot of discussion going on. As you may know, the "black box" nature of NNs make them a little more difficult than other means, when you have to validate them for safety critical systems.
        p1esk 4y ago
        If you use CV for autonomous driving you use object detectors, and any modern object detector uses a neural network as its backbone.
        f1shy 4y ago
        Used yes. But not the biggest part. Maybe 10% at most. We are doing L4, so if something goes wrong, the manufacturer is responsible. Once we were in a test car. It was behaving great. At one point, the car "makes the decision" to accelerate and pass. The manager to which we were showing asks "we did the car not waited a little bit? can we change that behavior?". A very long discussion ensued, about data sets, labeling, training... long story short: we have to be able to change behavior (even for perception) in a deterministic way. So the amount of NNs went down dramatically after that.
- jandrewrogers 4y ago
  Python for machine learning is a coincidence of history. Python is the traditional scripting language for high-performance computing (HPC) going back a very long time, as a wrapper for highly optimized and scalable C libraries. Python on supercomputers wasn't originally used for machine learning but it naturally included support for excellent linear algebra libraries, etc for various types of large-scale modeling. When machine learning first became trendy, data scientists that wanted access to mature library implementations of critical algorithms that worked very well at large scales were mostly limited to C/C++ or Python wrappers of that C/C++. Naturally, they chose Python for the same reason Python was already being used in HPC -- ease of use. By virtue of its use in supercomputing, Python had uniquely strong support for many types of machine learning before there was a demand for machine learning. If HPC had used some other popular scripting language instead of Python, we'd probably be using that for machine learning right now.

by Xeamek 4y ago

synergy20 4y ago

same can be said to typescript, did not mention them to make the point.

f1shy 4y ago

TBH I was shocked by "Python for machine learning". I do not know where that comes from. Maybe because some important toolkits are available for Neural Networks? But machine learning is not neural networks... you can do it with NNs, but there are so many other algorithms... I'm really surprise of this kind of posts in hacker news. I thought the audience here has other experiences.

plonk 4y ago
Python is the main language for PyTorch and TensorFlow, there are few ML frameworks more complete than Scikit-learn, I'm not aware of an equivalent to NumPy in other languages, and this list is probably missing other things I don't use. What's the alternative for quick ML prototyping?
- didibus 4y ago
  R, Matlab, Julia are some common alternatives. Java/Scala I've also seen, specifically things around Spark ML, and Spark compute. That said, Python is definitely the most common nowadays I'd say.
tinco 4y ago
That's literally the only one that didn't shock me. What universe do you live in where machine learning algorithms aren't tied together with Python? And what industry where machine learning is not mainly neural networks?
- f1shy 4y ago
  In the company I work most of it is done in matlab for "putting together" then goes to C++. Again not neural networks. I'm saying all the other machine learning. Maybe rare case.
  - p1esk 4y ago
    What kind of machine learning does your company do? It's probably something simple, because for anything complicated, like computer vision, speech recognition, any non-trivial NLP, and large scale recommender systems you do need neural networks.
    - f1shy 4y ago
      Autonomous driving, including, for example, computer vision. No NLP or speech. About the "need" or neural networks, there is a lot of discussion going on. As you may know, the "black box" nature of NNs make them a little more difficult than other means, when you have to validate them for safety critical systems.
      - p1esk 4y ago
        If you use CV for autonomous driving you use object detectors, and any modern object detector uses a neural network as its backbone.
        f1shy 4y ago
        Used yes. But not the biggest part. Maybe 10% at most. We are doing L4, so if something goes wrong, the manufacturer is responsible. Once we were in a test car. It was behaving great. At one point, the car "makes the decision" to accelerate and pass. The manager to which we were showing asks "we did the car not waited a little bit? can we change that behavior?". A very long discussion ensued, about data sets, labeling, training... long story short: we have to be able to change behavior (even for perception) in a deterministic way. So the amount of NNs went down dramatically after that.
jandrewrogers 4y ago
Python for machine learning is a coincidence of history. Python is the traditional scripting language for high-performance computing (HPC) going back a very long time, as a wrapper for highly optimized and scalable C libraries. Python on supercomputers wasn't originally used for machine learning but it naturally included support for excellent linear algebra libraries, etc for various types of large-scale modeling. When machine learning first became trendy, data scientists that wanted access to mature library implementations of critical algorithms that worked very well at large scales were mostly limited to C/C++ or Python wrappers of that C/C++. Naturally, they chose Python for the same reason Python was already being used in HPC -- ease of use. By virtue of its use in supercomputing, Python had uniquely strong support for many types of machine learning before there was a demand for machine learning. If HPC had used some other popular scripting language instead of Python, we'd probably be using that for machine learning right now.