I 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.
ML on Apple ][+
11–20 of 33 posts
Re: ML on Apple ][+
#12Since when did regression get upgraded to full blown ML?
When you find yourself solving NP-hard problems on an Apple II, chances are strong you've entered machine learning territory
Re: ML on Apple ][+
#13I 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?
Re: ML on Apple ][+
#14Bit of a weird choice to draw a decision boundary for a clustering algorithm...
Re: ML on Apple ][+
#15Since when did regression get upgraded to full blown ML?
Since when did ML get upgraded to full blown AI?
Re: ML on Apple ][+
#16I 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.
Re: ML on Apple ][+
#17Earlier quoted context omitted.
A million things. Diffusion, back propagation, attention, to name a few.
Back prop and attention are just extensions of interpolation.
By that logic it's all "just linear maths".
Back prop requires and limits to analytically differentiable in a normal way.
Attention is... Oh dear comparing linear regression to attention is comparing a diesel jet engine to a horse.
Re: ML on Apple ][+
#18One of my early "this is neat " programs was a genetic algorithm in Pascal. You entered a bunch of digits and it "evolved" the same sequence of digits. It started out with 10 random numbers. Their fitness (lower was better) was the sum the difference. So if the target was "123456" and the test number was "214365", it had a fitness of 6. It took the top 5, and then mutated a random digit by a random +/- 1. It printed…
I wrote a whole project in pascal around that time. Analyzing two datasets. It was running out of memory the night before it was due, so I decided to have it run twice, once for each dataset.
That's when I learned a very important principal. "When something needs doing quickly, don't force artificial constraints on yourself"
I could have spent three days figuring out how to deal with the memory constraints. But instead I just cut the data in half and gave it two runs. The quick solution was the one that was needed. Kind of an important memory for me that I have thought about quite a bit in the last 30+ years.
Re: ML on Apple ][+
#19Earlier quoted context omitted.
Back prop and attention are just extensions of interpolation.
By that logic it's all "just linear maths". Back prop requires and limits to analytically differentiable in a normal way. Attention is... Oh dear comparing linear regression to attention is comparing a diesel jet engine to a horse.
It's all just a series of S(S(S(....S(0)))) anyways.
Re: ML on Apple ][+
#20An Aeon ago in 1984, I wrote a perceptron on the Apple II. It was amazingly slow (20 minutes to complete a recognition pass), but what most impressed me at the time was that it did work. Since that time as a kid I always wondered just how far linear optimization techniques could take us. If I could just tell myself then what I know now...