Hybrid neural networks. - page 18
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The question remains... Where is the truth?
Upper left corner(RProp). Why DE/DW=0 when DE/DW(t-1)*DE/DW<0?
May I ask which article is this page from? Give its full title.
May I ask which article this page is taken from? Give its full name.
These are from different articles, I gave a link to RPROP in the author's version above.
May I ask which article is this page from? Give its full title.
Here.net 10-22-11-1
Rprop:(476 ep.)
Reverse + Momentum(n=0.01, a=0.9)(136 ep.):
Here.net 10-22-11-1
Rprop:(476 ep.)
Inverse + Momentum(n=0.01, a=0.9)(136 ep.):
RProp is inefficient :)
What's in, what's out?
RProp is inefficient :)
What's in, what's out?
No.
10 open -> 1 open
It's fun to watch the collective experiments with neural networks. How many times do I have to tell you: a neural network is the same tool as RSI and Stochastic and many others, the only difference being that they are incredibly complex. You won't solve your problems with neural networks... It's just a waste of time... All those who have not made conclusions for themselves, let them ignore this remark.
It's fun to watch the collective experiments with neural networks. How many times do I have to tell you: a neural network is the same tool as RSI and Stochastic and many others, the only difference is that they are incredibly complicated. You won't solve your problems with neural networks... It's just a waste of time... All those who haven't made a conclusion for themselves, let them ignore this remark.
It's a first-year course at university. I actually went through it in high school. Only the teacher is important, that is essentially the type of error in the output of the network.
Cue learning algorithms (not evr.)
All gradient - LMA, quasi-newtonian. Genetic ...
Heuristics - all RProp variants, "annealing method" etc.