That answers some questions. When trying to implement NNs myself, I was amazed by simplicity of functions used in typical NNs. And I tried to utilize some more complex functions, at least activaton ones. In the result, I came to the same conclusion: more complex activation functions demand more compute, but their benefit is quite not obvious. So, in the end I used ReLu or something similar. I use NN to sort some images on my pc. The goal is to rank them according to the likelihood that I'll find those images visually pleasing. Program works ok, it does the job.
That's great although I think using NN with small data and small training (e.g., on your computer) is likely to be overly complex. Their strength is mostly on HUGE data with MASSIVE compute due to the scaling laws.
@@finxter That's true. Real power is in big numbers. However, my experience shows that some significant estimations can be made even on simple networks. For me, the network has to vagely recognize the scene and asess image quality. I suppose that can be done even on moderately sized networks. As for training dataset, mine counts tens of thousands images. Dataset has only two categories: accepted and regected. And program has to guess likelihood that given image will be in one of those categories. On other words, NN gives a number between 0 and 1, but tries to be as close to a expected answer as possible. As I said, results are quite acceptable. One notable obstacle is that I have no Nvidia card, so that had to be set up to work on Radeon and under Windows. Still, it works and I'm content.
Fair. For instance check out this one for a more technical explanation (without memes as neurons): ruclips.net/video/CkCijaXqAOM/видео.htmlsi=90V0Vj11XlRhnKvD
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
That answers some questions.
When trying to implement NNs myself, I was amazed by simplicity of functions used in typical NNs. And I tried to utilize some more complex functions, at least activaton ones. In the result, I came to the same conclusion: more complex activation functions demand more compute, but their benefit is quite not obvious. So, in the end I used ReLu or something similar.
I use NN to sort some images on my pc. The goal is to rank them according to the likelihood that I'll find those images visually pleasing. Program works ok, it does the job.
That's great although I think using NN with small data and small training (e.g., on your computer) is likely to be overly complex. Their strength is mostly on HUGE data with MASSIVE compute due to the scaling laws.
@@finxter That's true. Real power is in big numbers.
However, my experience shows that some significant estimations can be made even on simple networks. For me, the network has to vagely recognize the scene and asess image quality. I suppose that can be done even on moderately sized networks.
As for training dataset, mine counts tens of thousands images. Dataset has only two categories: accepted and regected. And program has to guess likelihood that given image will be in one of those categories. On other words, NN gives a number between 0 and 1, but tries to be as close to a expected answer as possible. As I said, results are quite acceptable.
One notable obstacle is that I have no Nvidia card, so that had to be set up to work on Radeon and under Windows. Still, it works and I'm content.
If you are curious about KANs, find a different source than this video.
Fair. For instance check out this one for a more technical explanation (without memes as neurons): ruclips.net/video/CkCijaXqAOM/видео.htmlsi=90V0Vj11XlRhnKvD