This motivates me to try this on my Ministrel 4th (21th century Jupiter Ace clone).
ML on Apple ][+
21–30 of 33 posts
Re: ML on Apple ][+
#22I thought this was going to be about the programming language, and I was wondering how they managed to implement it on a machine that small.
Same. What flavor of ML would be the most appropriate for that challenge, do you think?
While not exactly ML, David Turner's Miranda system is pretty small, and might be feasible:
Re: ML on Apple ][+
#23Re: ML on Apple ][+
#24I thought this was going to be about the programming language, and I was wondering how they managed to implement it on a machine that small.
That's also what I was thinking. ML predates the Apple II by 4 years, so I think there is definitely a chance of getting it running! If targetting the Apple IIGS I think it would be very achievable; you could fit megabytes of RAM in those.
Likely any early implementation of ML would have been on a mainframe or minicomputer, not a 6502. A mainframe/minicomputer would have had oodles of storage (both durable and RAM), as well as a compiler for a high level language (which fits what I can see in https://smlfamily.github.io/history/ML2015-talk.pdf and other locations).
Re: ML on Apple ][+
#25I thought this was going to be about the programming language, and I was wondering how they managed to implement it on a machine that small.
That's funny, pretty sure we used Standard ML on the old oscilloscope Macs in undergrad. Not Apple 2 of course, but still already pretty dated even at that time (late 90s).
Re: ML on Apple ][+
#26I'm surprised no one else has commented that a few of the conceptual comments in this article are a bit odd or just wrong.
> The final accuracy is 90% because 1 of the 10 observations is on the incorrect side of the decision boundary.
Who is using K-means for classification? If you have labels, then a supervised algorithm seems like a more appropriate choice.
> K-means clustering is a recursive algorithm
It is?
> If we know that the distributions are Gaussian, which is very frequently the case in machine learning
It is?
> we can employ a more powerful algorithm: Expectation Maximization (EM)
K-means is already an instance of the EM algorithm.
Re: ML on Apple ][+
#27Applesoft BASIC is just so darn readable. Youngsters have nothing comparable these days to learn the basics of expressing an algorithm without having to know a lot more.
And if it ever became too slow, you could reimplement the slow part in 6502 assembler, which has its own elegance. Great way to learn, glad I came up that way.
Re: ML on Apple ][+
#28Earlier quoted context omitted.
That's also what I was thinking. ML predates the Apple II by 4 years, so I think there is definitely a chance of getting it running! If targetting the Apple IIGS I think it would be very achievable; you could fit megabytes of RAM in those.
Likely any early implementation of ML would have been on a mainframe or minicomputer, not a 6502. A mainframe/minicomputer would have had oodles of storage (both durable and RAM), as well as a compiler for a high level language (which fits what I can see in https://smlfamily.github.io/history/ML2015-talk.pdf and other locations).
So I've been mildly nerd sniped. It looks like the first target was a PDP-10 [1]. It ran Stanford Lisp used by the "DEC 10" implementation of ML. The architecture is pretty unusual by modern standards, but it doesn't look to be that powerful and seems to top out at around 1MB of RAM. Next up we have a VAX [2] implementation. It's not clear which specific system it was originally developed for, but we're talking early 80s so it probably wasn't much more powerful than the PDP-10. Either way, I think a maxed Apple IIGS with a hefty 8MB of RAM and perhaps overclocked to 14MHz is more than enough raw power to handle ML. Unfortunately I haven't been sufficiently nerd sniped to actually implement this. I leave that as an exercise for the reader ;-)
Re: ML on Apple ][+
#29Earlier quoted context omitted.
Likely any early implementation of ML would have been on a mainframe or minicomputer, not a 6502. A mainframe/minicomputer would have had oodles of storage (both durable and RAM), as well as a compiler for a high level language (which fits what I can see in https://smlfamily.github.io/history/ML2015-talk.pdf and other locations).
So I've been mildly nerd sniped. It looks like the first target was a PDP-10 [1]. It ran Stanford Lisp used by the "DEC 10" implementation of ML. The architecture is pretty unusual by modern standards, but it doesn't look to be that powerful and seems to top out at around 1MB of RAM. Next up we have a VAX [2] implementation. It's not clear which specific system it was originally developed for, but we're talking early…
an enormous amount of software was developed on the PDP-10 and PDP-11 and later VAX systems that could not have been done on microcomputers in the day. You can't just compare raw RAM and clock rates, the PDPs were set up for multi-user productivity on complex problems and had a wide range of system software to enable building and deploying advanced software.
Re: ML on Apple ][+
#30You don't even need a computer for ML [0]!