![]() If not, this would be for sure a huge disadvantage. You may know that GPUs are the way to go today for large problems since they are much faster than CPUs for deep learning and I'm not sure if you can use a GPU together with Octave. Writing fast code: Most libraries fall back to fast C++ routines (BLAS / LAPACK) when it comes to heavy numerical computations or implement functionality in C++ straight away.I have to admit, I never used Octave (but I have experience with Matlab), but I doubt you can write a simple network model + training / evaluation loop faster than with keras or pytorch. Writing code fast: Beating any of the python libraries seems to be a difficult task, especially keras.When you say " fast" there are two interpretations, writing code fast and writing fast code. Here are my thoughts on why that is the case: Although you can use Octave for machine learning ( nnet package), I doubt you could use it for " faster coding" of neural networks. As far as I know GNU Octave is an open-source alternative for Matlab. ![]()
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