## Neural Nets

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Got two books on Neural Nets, neither cover noting more than 1 hidden layer.

Oh, anyone recombed a book, really would like one that covered two or more hidden layers..

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Last Edited: Sat. May 9, 2020 - 09:50 PM
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Anyone here good with Neural Nets or equally really good at maths.  Have a look at the attached document with the full question listed!

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try this...has code you can try & you can instal Python for free  (get Python at  https://www.python.org/)

https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/

I  somewhat remember a course I took around 1993 using my 100 MHz PC to train on Lenna:

https://en.wikipedia.org/wiki/Lenna

When in the dark remember-the future looks brighter than ever.   I look forward to being able to predict the future!

Last Edited: Sun. Apr 19, 2020 - 03:59 PM
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Hello freaks.  So I was wondering if you could help me?  I'm looking to know how to calculate a 2 hidden layer network.  Have a look at the diagram below.

To calculate w1 I have the following.

The calculation of the first term on the right hand side of the equation above is a bit more involved than previous calculations since affects the error through both and .

Now my question is.  If I had a hypothetical weight input into X1 called weight I, wi what is the equation for back propagating tht weight? I'm thinking it is maybe it is this.

âˆ‚E/âˆ‚wi = âˆ‚E/âˆ‚x1  .  âˆ‚x1/âˆ‚zx1  .  âˆ‚zx1/âˆ‚w1

Therefore:

âˆ‚E/âˆ‚x1 = ( âˆ‚E/âˆ‚o1 .  âˆ‚o1/âˆ‚zo1  .  âˆ‚zo1/âˆ‚x1)  +  (âˆ‚E/âˆ‚o2  .  âˆ‚o2/âˆ‚zo2  .  âˆ‚zo2/âˆ‚x1)

Or maybe it is this:

âˆ‚E/âˆ‚x1 = ( âˆ‚E/âˆ‚h1 .  âˆ‚h1/âˆ‚zh1  .  âˆ‚zh1/âˆ‚x1)  +  (âˆ‚E/âˆ‚h2  .  âˆ‚h2/âˆ‚zh2  .  âˆ‚zh2/âˆ‚x1)

I really hope you can help me with this, thanks for having a look!

Wm.

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Ah, I didn't see this thread before I replied to your other one here:

https://www.avrfreaks.net/comment/2896386#comment-2896386

... so never mind ;-)

 "Experience is what enables you to recognise a mistake the second time you make it." "Good judgement comes from experience.  Experience comes from bad judgement." "Wisdom is always wont to arrive late, and to be a little approximate on first possession." "When you hear hoofbeats, think horses, not unicorns." "Fast.  Cheap.  Good.  Pick two." "We see a lot of arses on handlebars around here." - [J Ekdahl]

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This function does it, just substitute for db1 for the weights.