matter with what i want to do so you might have like a bunch of images of digits right each with their label the supervised learning process involves feed one of these in what does the neural network guess like if you feed in the zero digit and it guesses it's a four nope that's not right go back and uh adjust the neural network according to the error and rinse and repeat so what do i mean by adjust according to the error i mean i'm hand waving a ton of stuff here i think i have probably a NUMBER_TOKEN part series that kind of goes through all of this much more methodically step by step and writing the code for it but the the thing that's important here is that each one of these connections is what's known as a weighted connection so there's a weight and i think an analogy that i've heard is that you can think of each connection like a dial on you and that weight can be tuned up or down you know what is the relative importance of this particular input um as related to some particular output and so as you feed this the training data through if i fed in this digit of a one and the neural network output said one there's no need to change any of the weights because it got it correct but then if i fed this next one in of a one and it gets to nine i've got to adjust the weights and the process of doing that with large data sets over long periods of time yields a sort of optimized set of weights that would in an ideal world will perform well with new data data that's not part of the training set there's so much more to say about this but i just wanted to sort of talk through the very basic uh um kind of process of what it means to train a neural network for image classification let me take a pause a short pause here and look over and see if there are any questions in the chat and i also want to um i also want to check what time it is because uh yeah um and um sorry okay i see that there's a discussion about shaders that was last stream i by the way i'm so obsessed with shaders and i'm i do have a fantasy of doing a whole like shaders tutorial kind of like learn shaders with me might be the title of it or something but um this has been this is part of the course that i'm teaching right now and i've been talking about auto encoders and generative models all semester and i really want to have a running example that i can kind of demonstrate so um all right supervised means we have correct output layers france francello yes supervised learning means that you have a training data set a training a labeled training data set so you are supervising the learning process with the input data and the correct answers can you complete your diagram second question good resources for fast iteration of neural networks and machine learning i don't know that actually sorry i'm reading the questions in real time i don't actually unders i don't know if i understand that question um but um hopefully the chat people can provide some good resources you know if you're interested you know not to plug things but i guess that's what i'm that's
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