machinery for training of neural networks. Now one more note I would like to make at this stage is that as you see here micrograd is a scalar valued autograd engine so it's working on the you know level of individual scalars like negative 4 and 2 and we're taking neural nets and we're breaking them down all the way to these atoms of individual scalars and all the little pluses and times and it's just excessive and so obviously you would never be doing any of this in production. It's really just done for pedagogical reasons because it allows us to not have to deal with these n-dimensional tensors that you would use in modern deep neural network library. So this is really done so that you understand and refactor out back propagation and chain rule and understanding of neural training and then if you actually want to train bigger networks you have to be using these tensors but none of the math changes, this is done purely for efficiency. We are basically taking all the scalars all the scalar values we're packaging them up into tensors which are just arrays of these scalars and then because we have these large arrays we're making operations on those large arrays that allows us to take advantage of the parallelism in a computer and all those operations can be done in parallel and then the whole thing runs faster but really none of the math changes and they're done purely for efficiency so I don't think that it's pedagogically useful to be dealing with tensors from scratch and I think and that's why I fundamentally wrote micrograd because you can understand how things work at the fundamental level and then you can speed it up later. Okay so here's the fun part. My claim is that micrograd is what you need to train neural networks and everything else is just efficiency so you'd think that micrograd would be a very complex piece of code and that turns out to not be the case. So if we just go to micrograd and you'll see that there's only two files here
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/audio.html