Blaise Aguera doing a pretty good 101 in neural nets

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One of those cross discipline,  same terminology deals? Intelligence in the ai vernacular is like a black box version of software but much more powerful. We understand why it works but the actual state,  in old money the program logic, is something that just happens. It learns. It learns to perform intelligent operations on input data. It does stuff that would take millions of coders millions of years to hand code. If it could even be hand coded at all.

Considering the impact the olde fashioned, hand coded shit just had on the planet, I'm fast approaching certainty that no one aint seen nothing yet.

Enjoying all the links here.  What I got is before, in ancient procedural procedural terms, you might build some object detection code as a composite of various hand-picked relevant feature detectors; now, you "train" a model to learn, for itself, which detector-like-filters work to best fit input data to output.  During training, some models learn by back-propagation, so the algorithm does a backward pass, starting from a list that already specifies the correct answer or output (y) it changes the weights of filters (W) to match the given input (X).  I think, though it still beats me.

[P3nT4gR4m]

Would say that pretty much sums up whats going on with back propagation as I understand it. The hard part for me, as a coder, was letting go and not needing to be able to read the finished logic. It was a kind of jarring transition, coming from a background of having to micromanage every fucking step in the machines "thought process" 

[Brother Mythos]

These 3 vids are the ones that I found the most useful in helping me grasp the concept. I was coming at it from a programmers point of view - "How do I code it" Turns out that was the wrong question. You don't code it, it just works.

CNN's are only part of the picture but they're a good starting point. Now I I can grok them, a lot of the other stuff makes more sense.

https://youtu.be/l42lr8AlrHk

https://youtu.be/C_zFhWdM4ic

https://www.youtube.com/watch?v=py5byOOHZM8

I reviewed those YouTube clips and they did help me gain more of an understanding of ANNs.

I also reviewed the following Wikipedia article on ANNs: https://en.wikipedia.org/wiki/Artificial_neural_network

In the ‘External Links’ section of the above article they list the following link: http://www.dkriesel.com/en/science/neural_networks

On that site you can download the book, A Brief Introduction to Neural Networks by David Kriesel, in PDF form. I downloaded the book, read a number of chapters in depth, and skimmed through the rest. Overall, I found the book very helpful.

That Wikipedia article on ANNs also led me to their article on Machine learning: https://en.wikipedia.org/wiki/Machine_learning

This article was also helpful, and the ‘Software’ section lists ‘Free and open-source software’. Seeing that, I went over to SourceForge and searched ‘artificial neural network’. To my surprise, I got 751 program hits!

Here’s my SourceForge link: https://sourceforge.net/directory/os:windows/?q=artificial%20neural%20network

So, on the surface, it appears that I just might be able to treat an ANN software package like an electrical engineer’s ‘black box’, and make it work for me as long as I can figure out how to program the inputs and outputs.

Now I have to decide if I want to try to take my new found, but superficial knowledge to the next level. Just for the hell of it, I did some ‘if/then’ AI programming for an open-source game a few years ago. So, I do have some programming skills, and the right ANN software package just might be adaptable to that particular open-source game, or another one I’m familiar with. Still, as I only get the urge to write code on a ‘once in a blue moon’ basis, I have to go through a ‘relearning curve’ every time I do it.

Anyway, I now know a little bit about Artificial Neural Networks. So, thanks for your original and your follow-up posts.