lun apr 19 23:45:15 CEST 2010
An introduction to neural networks with an implementation using
my Neural++ library
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I've just released a new version of my paper about the analysis
and design of neural networks. A new part has been inserted about
the theoretical background of supervised networks, referring to
the perceptrons, single-layer and multiple-layer networks, with
Widrow-Hoff or back-propagation training algorithms. A new ver-
sion may soon arrive too, as I'm getting quite interested to the
not supervised networks and I'm reading a lot about these net-
works. It would be interesting if I could implement some not su-
pervised algorithms in my Neural++ library. Moreover, I'm also
interested in introducing in my library a robust self-adjustment
of both learning rate and inertial momentum, so letting the
synaptic weights self-adjust in the learning process and converg-
ing to the desired result in a shorter number of steps.
The link is here.
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GAT d? a? C++++ U++++ P++++ L+++++ E--- W+++ !w PS+++ PE-- Y++ PGP++ X++
R+ tv-- b+>+++ DI++ G++ e+++ h* r++ z**
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