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I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes ov
by harel 9y ago
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site called ai-junkie which used (does?) have very practical and layman docs about A.I. before ML became the buzz word du jour.
- srean 9y agoOh I do understand you. The manual for driving a car has to be different from the manual for designing a car. I believe you are looking for a manual to drive the car where you don't really need a lot of visibility into the inner workings. A problem is that ML is not quite as mature as a car yet, so the driving manuals will be a bit on the thinner / shallower side. > I understand how neural networks work Quickly write that down please, that would do the world a favor. Researchers are still grappling with the question 'why the hell does this freaking thing work as well as it does, when it does'.
- harel 9y agoHaha, I meant, the practical idea behind their usage. The example of OCR via a NN was very good in drilling that concept into my head (many inputs leading down to output). The HOW (in capitals) they work bit - I'm not going there. This thread here got me searching and I'm reading through the basic TensorFlow docs. That's, so far, sinking in.
- jclos 9y agoHonestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
- srean 9y agoCannot upvote this enough. I think this is the only way to really grok backpropagation. The hours of staring at the update formula till your eyes glaze over the subscripts and superscripts and the summations would not give you as good an understanding as implementing a toy neural net with just a single hidden layer. Its actually a whole lot easier than parsing those low-level notation. It can be done better with high level notation but then you would need familiarity with the relevant mathematical abstractions.
- jclos 9y agoYes, once you've dealt with implementing it, the notation just makes sense as the most compact way of formalizing what you just did.
- annnnd 9y agoI respectfully disagree. Of course you need to really understand backpropagation for any advanced stuff, but it is easier to ignore it at the beginning. Take Keras container, copy some MNIST example from somewhere and tweak it. Then, when you have a general feel of how training NNs works, gradually learn about each concept - BP should of course be one of the first. By the time you get into math stuff it will probably make much more sense because you will understand how it applies to your case. But I guess the approach depends on how you best learn, so there is no wrong answer. Just jump in!
- harel 9y agoGood idea! I'll give it a go
- RandyRanderson 9y agoDon't bother. The guy that wrote Encog has several yt tutorials on how he did it and there are hundreds of others. I learned nothing from impl my own other than why all NN libs break when you input values > 1. Any decent NN lib will be way better that whatever you could write in a week full time. Pick your fav lang, find the most used NN lib and try a kaggle competition. If you can get to about rank 50% your training is complete.