3 ms·
You got that wrong, the problem with multilayer networks at the time was not about scaling, but how to train such a network at all. Using error backpropagation
by _0ffh 3y ago
You got that wrong, the problem with multilayer networks at the time was not about scaling, but how to train such a network at all.
Using error backpropagation for training multilayer networks was what overcame that problem, not Moore's law, or anything else.
- gumby 3y agoDon't you remember what machines were like back then? The PDP-10 was about a 400 MIPS timesharing computer with an 18-bit address space. People like Rumelhart kept at it, and eventually the hardware caught up with the requirements.
- _0ffh 3y agoI know computers were slow, and Moore's law was what made really big networks computationally feasible. Still, the decisive algorithmic breakthrough for the Perceptron was applying BP to MLPs. Without multiple layers you can't solve problems which aren't linearly separable, and without error backpropagation you can't train multilayer networks.