working well and then if c gets bumped a tiny amount in h by h then of course a times b is unaffected and now c becomes slightly bit higher what does that do to the function it makes it slightly bit higher because we're simply adding c and it makes it slightly bit higher by the exact same amount that we added to c and so that tells you that the slope is one that will be the the rate at which d will increase as we scale c okay so we now have some intuitive sense of what this derivative is telling you about the function and we'd like to move to neural networks now as i mentioned neural networks will be pretty massive expressions mathematical expressions so we need some data structures that maintain these expressions and that's what we're going to start to build out now so we're going to build out this value object that i showed you in the readme page of micrograd so let me copy paste a skeleton of the first very simple value object so class value takes a single scalar value that it wraps and keeps track of and that's it so we can for example do value of 2.0 and then we can get we can look at its content and python will internally use the wrapper function to return this string like that so this is a value object that we're going to call value object", metadata={'source': 'example_data/Lecture_1_0.mp3'})]
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/audio.html