I want to crack Neural Networks

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I want to crack Neural Networks
« on: January 10, 2018, 01:52:17 pm »
Hi friends. I'm doing a tad better.

I find it fascinating that one piece of information (shared features) makes neural networks make so much more sense to me. Now I get why Wiki I read many months back said the brain processes higher concepts as it goes higher layers up. And I get the shared features part now, and why they can detect small and whole features and in the end ex. a thousand images. And I've learned before that language works the same as a ex. CNN where a b c is used lots then words light up less so then word pairs less-less so and higher up until bigger topics are recognized consciously not subconsciously. FURTHER the frontal cortex is based like this but allows higher concepts like if you see this image and this image and this sentence and do these actions then one of the output neurons lights up. And am I right to say then that all neural networks are hierarchical and work by "shared features"? Tell me more things as important as "shared features".

Also after the above question, I want to draw out what a neural network looks like visually, to understand how it learns to sense, act, and reward those actions.

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ranch vermin

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Re: I want to crack Neural Networks
« Reply #1 on: January 10, 2018, 05:10:19 pm »
I havent got mine working yet,   but you have to build your way up to the big concepts,  u cant detect it all at once considering your starting from an rgb map (the camera.)

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Re: I want to crack Neural Networks
« Reply #2 on: January 10, 2018, 05:23:57 pm »
Lol are you talking about my studying or the AI's studying?

To be clearer, you only recognize a face if you recognize a nose, mouth, etc, first. It's a hierarchy, using Shared Features. Are most Artificial Neural Networks based on a Shared Features Connectom? Vision, Language, and the Frontal Cortex seem to all be one.

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ivan.moony

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Re: I want to crack Neural Networks
« Reply #3 on: January 10, 2018, 05:30:48 pm »
They say that they are not certain how a neural network really does the big picture. They clued up small parts, combined a lot of them, and when they run the algorithms, it works, somehow. Maybe I missed something...
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Re: I want to crack Neural Networks
« Reply #4 on: January 10, 2018, 07:05:38 pm »
Oh, u mean how hierarchical backprop nets work.      they make groups of pixels, then groups of those groups, they can overlap or not overlap, its a brain teaser to think about.

Just imagine a really advanced one that groups pictures of the actual thing, with the word of the thing, or sign language of the thing as well in different sub categories, all unsupervised,    it gets pretty complex.

After youve done this job, u then use it as a base for the robot to "see the world through" and then you generate movements off it to its motors.

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Re: I want to crack Neural Networks
« Reply #5 on: January 10, 2018, 07:47:28 pm »
Finally I feel like I'm on topic with you guys....................

Yes yes! So Deep Learning NN...great.....it recognizes. But its outputs must now go to a motor DL NN. There's a lot more of the bigger picture missing. Like an attention module. A history bar over-lording all the hierarchies as a huge sequential parallel hierarchy and frontal cortex all at once. Reward module. Artificial Reward creation. My mind is exploding full of ideas.

Now I want to show yous an image and I want to know how it is rewarding its actions. Image atteched.

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Re: I want to crack Neural Networks
« Reply #6 on: January 11, 2018, 09:51:18 am »
thats the idea.  Korrelan has a neural network that could possibly produce a blob map of identifications,   so u could be working with something like that for the input into the motor generation system.     My model reduces it to so many points of interest.

Also, I have never seen a robot that actually saw its environment in this detail, so its *new* if you get it to work!

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keghn

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Re: I want to crack Neural Networks
« Reply #7 on: January 11, 2018, 02:05:40 pm »

The 8 Neural Network Architectures Machine Learning Researchers Need to Learn: 
https://towardsdatascience.com/the-8-neural-network-architectures-machine-learning-researchers-need-to-learn-11a0c96d6073 


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Re: I want to crack Neural Networks
« Reply #8 on: January 11, 2018, 06:14:05 pm »
Thanks keghn that clarified some things.

So LSTMs+GANs+HopFieldNNs is the current furthest thing we have, since they combine FNN, RNN, and HNN. Right?



"To be clearer, you only recognize a face if you recognize a nose, mouth, etc, first. It's a hierarchy, using Shared Features. Are most Artificial Neural Networks based on a Shared Features Connectom? Vision, Language, and the Frontal Cortex seem to all be one."

Still don't have an actual answer for this question.

Do all Artificial Neural Networks use hierarchies and build higher concepts made of smaller ones? Or do some NNs revolve around other things and don't even use concept building? If so, please tell me what it is. Because I find the "using shared features to use smaller concepts to form higher concepts" really important. So, I want to know more things this important. Ex. pools but I don't think Pools are THAT important for getting the WHOLE picture if you get what I mean.

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Re: I want to crack Neural Networks
« Reply #9 on: January 11, 2018, 07:10:28 pm »
your building bigger groups from smaller groups.

If u have object a and object b,  they can become a single object due to a symantic measurement.   face would be eyes and nose as in "contains".

But it gets complicated.

Hamburger would be the same thing as a chainsaw,  if the symantic were "they both destroy the environment."   - as in cows farting into the atmosphere, being what some stupid hippies think.   My point is, theres alot of relations to draw to make a container due to a context, or measurement.


So if the computer needed something that destroyed the environment. (although alot more would be taken into account)  it has the knowledge in its database to do it,  even if its just a scoring sharing around containers in its ga type randomization system.

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ivan.moony

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Re: I want to crack Neural Networks
« Reply #10 on: January 11, 2018, 07:46:47 pm »
An object is a set of properties by which the object is recognized. Like in "object oriented programming" (OOP), property membership can be inherited over different classes. Classes, beside inheritance properties, can add their own specific properties. "Cow" inherits all the properties from "living being", which inherits all the properties from "entity", and so on. Two classes (and so objects being members of classes) can share the same  property inherited form super-class (like "living being), although they are different classes (like "cow" or "giraffe"). "living being" may have a property "farts", while "cow" and "giraffe", that inherit "farts", may add their own properties like "produces tasty milk" or "has problems drinking water". And the pyramid branches downwards by growing the membership of (inherited) properties. Going up through the pyramid is called generalization, and going down is called specialization.

Object oriented programming can form nice knowledge structures, especially when paired to declarative programming (which is still not an often case, but OCaml is an exception).

[Edit] Building pyramidal knowledge bases is still in scientific discovery stage, IMHO (see semantic web and OWL).
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Re: I want to crack Neural Networks
« Reply #11 on: January 11, 2018, 08:22:02 pm »
But do all neural nets use "Features"? I.e. nose and eye Features create face Feature. And connections all have different weight probabilities. ?

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keghn

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Re: I want to crack Neural Networks
« Reply #12 on: January 11, 2018, 08:42:20 pm »

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Re: I want to crack Neural Networks
« Reply #13 on: January 12, 2018, 12:18:03 am »
That didn't really help keghn lol......I already got a ton of that actually. Oh I see it has more parts. Ok so it's quite visually useful and good quality stuff.

But I mean, the question below is still unanswered:

"But do all neural nets use "Features"? I.e. nose and eye Features create face Feature. And connections all have different weight probabilities. ?"

I get that there's more to NNs....but....I want to know if they ALL use feature sharing to build deeper higher concepts. I would think not all NNs use it. But, maybe they all do.

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keghn

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Re: I want to crack Neural Networks
« Reply #14 on: January 12, 2018, 06:55:25 pm »

 


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