Path: utzoo!censor!geac!torsqnt!lethe!yunexus!ists!helios.physics.utoronto.ca!news-server.csri.toronto.edu!bonnie.concordia.ca!thunder.mcrcim.mcgill.edu!snorkelwacker.mit.edu!bu.edu!olivea!samsung!sdd.hp.com!uakari.primate.wisc.edu!aplcen!jhunix!ins_atge From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards) Newsgroups: comp.ai.neural-nets Subject: Re: Nerual nets and Ada? Summary: APL as a neural net hacking language Message-ID: <7312@jhunix.HCF.JHU.EDU> Date: 8 Jan 91 19:10:20 GMT References: <436.27876349@vger.nsu.edu> Organization: The Johns Hopkins University - HCF Lines: 21 In article greenba@gambia.crd.ge.com (ben a green) writes: >In article <436.27876349@vger.nsu.edu> g_harrison@vger.nsu.edu writes: > I have had some interesting results but have been told that Ada and Neural Nets > are "mutually exclusive," "flawed in concept," etc. - by some "experts" in the > field. >Unfortunately, there are still many people stuck in Fortran, and it will be >a long, long time before the C folks learn what they're missing. Actually, I have recently been pseudo-forced to learn APL for a certain job involving neural networks. It actually is a really neat hacky language for mathematics. Matrix algebra can easily be done in just a few operators on a single line, and its workspace environment (and interpretive nature) make throwing together a network architecture easy. Sure, it's slow, uses wierd characters which you need to stick on your keyboard, and is not the most easily readable language. But it is a good method of prototyping neural architectures (before you actually code them in C or what-have-you). -Tom