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It's the .js port of FANN (Fast Artificial Neural Network) : http://leenissen.dk/fann/wp/ http://leenissen.dk/fann/wp/ > Fast (up to 150 times faster execution

It's the .js port of FANN (Fast Artificial Neural Network) : http://leenissen.dk/fann/wp/ http://leenissen.dk/fann/wp/ > Fast (up to 150 times faster execution than other libraries)

the8472 11y ago
> up to
- toxicFork 11y ago
  Sure, but the case for this prject isn't "Neural network implementation in javascript that's 'fast'". It's "FANN".js, where FANN expands to "Fast Artificial Neural Network", and the c implementation of FANN was probably the fastest when it was created. Conclusion: FANN.js is "the js port of FANN".
  - the8472 11y ago
    The F stands for Fast, and I don't even see any proof for their claim to fastness in the C version.
    - toxicFork 11y ago
      http://prdownloads.sourceforge.net/fann/fann_doc_complete_1.0.pdf?download http://prdownloads.sourceforge.net/fann/fann_doc_complete_1.... This one seems to have some information under "performance analysis". I'm not assuming it's correct, but go ahead and research further if you like, rather than quoting parts of my quote.
      - the8472 11y ago
        Besides the fann library, I will benchmark Jet's Neural Library [Heller, 2002] (hereafter known as jneural) and Lightweight Neural Network [van Rossum, 2003] (hereafter known as lwnn). I have made sure that all three libraries have been compiled with the same compiler and the same compile-options. I have downloaded several other ANN libraries, but most of them had some problem making them difficult to use. Either they where not libraries, but programs [Anguita, 1993], [Zell, 2003], they could not compile [Software, 2002], or the documentation was so inadequate that is was not possible to implement the features needed in the benchmark [Darrington, 2003]. Even though I will only benchmark two libraries besides the fann library, I still think that they give a good coverage of the different libraries which are available. I will now briefly discuss the pros and cons of these two libraries. Yeah, so the author picked two simple, "easy to use" NN libraries to benchmark against, had trouble with others and just dropped them and then claimed it's fast because it performed "up to" 140 times faster than one of those two libraries. Seems kinda like a "get a paper out" work that is technically correct but doesn't measure up to real world standards. Real implementations would use vectorized math, math libraries with hand-optimized assembly or run straight on the GPU.

It's the .js port of FANN (Fast Artificial Neural Network) : http://leenissen.dk/fann/wp/ http://leenissen.dk/fann/wp/ > Fast (up to 150 times faster execution than other libraries)

the8472 11y ago

> up to

toxicFork 11y ago
Sure, but the case for this prject isn't "Neural network implementation in javascript that's 'fast'". It's "FANN".js, where FANN expands to "Fast Artificial Neural Network", and the c implementation of FANN was probably the fastest when it was created. Conclusion: FANN.js is "the js port of FANN".
- the8472 11y ago
  The F stands for Fast, and I don't even see any proof for their claim to fastness in the C version.
  - toxicFork 11y ago
    http://prdownloads.sourceforge.net/fann/fann_doc_complete_1.0.pdf?download http://prdownloads.sourceforge.net/fann/fann_doc_complete_1.... This one seems to have some information under "performance analysis". I'm not assuming it's correct, but go ahead and research further if you like, rather than quoting parts of my quote.
    - the8472 11y ago
      Besides the fann library, I will benchmark Jet's Neural Library [Heller, 2002] (hereafter known as jneural) and Lightweight Neural Network [van Rossum, 2003] (hereafter known as lwnn). I have made sure that all three libraries have been compiled with the same compiler and the same compile-options. I have downloaded several other ANN libraries, but most of them had some problem making them difficult to use. Either they where not libraries, but programs [Anguita, 1993], [Zell, 2003], they could not compile [Software, 2002], or the documentation was so inadequate that is was not possible to implement the features needed in the benchmark [Darrington, 2003]. Even though I will only benchmark two libraries besides the fann library, I still think that they give a good coverage of the different libraries which are available. I will now briefly discuss the pros and cons of these two libraries. Yeah, so the author picked two simple, "easy to use" NN libraries to benchmark against, had trouble with others and just dropped them and then claimed it's fast because it performed "up to" 140 times faster than one of those two libraries. Seems kinda like a "get a paper out" work that is technically correct but doesn't measure up to real world standards. Real implementations would use vectorized math, math libraries with hand-optimized assembly or run straight on the GPU.