I'm completely new at ML, but really interested. To be honest, read many articles about it, but still don't understand the workings of it.
I just started to understand this example: https://storage.googleapis.com/tfjs-examples/mnist/dist/index.html
My thinking about it is that TF has some resources, some examples of how numbers look like, and try to match them with the ones in the test. I saw that sometimes the test changes a right prediction to a wrong, but makes better and better predictions. But how? I think that the program doesn't know the right predictions (and this way it won't know the wrong ones). In the training how it makes better predictions? Test by test, from what exceptions it will change it's predictions? What happens in a new test?