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Choosing a language for a class has different constraints than choosing one for research or production systems. I imagine performance is less important while ea
by tikhonj 15y ago
Choosing a language for a class has different constraints than choosing one for research or production systems. I imagine performance is less important while ease of learning the language is probably worth much more (probably means not using Haskell for a class :().
I think the ML class at my university--it's in "beta" right now--is using Python following similar logic.
- tensor 15y agoThe place where performance really matters is generally in the core learning algorithms. Since these are all written in C or Java, you can very easily get away with programming the rest of the code in a higher level language. This is why Python is a feasible choice for machine learning at all. That said, Clojure is certainly a lot faster than Python for native code and the performance gap with Java is continuing to drop. It's also worth pointing out that you should not ignore C or C++ libraries just because your are running on the JVM. It's not very hard to interface to a core C library with JNI, although be warned, there is a slight trick to doing this via clojure rather than Java.