Quotes About Neural net

Whatever input it's given, the neural net is generating an answer. And, it turns out, to do it in a way that's reasonably consistent with what humans might do. As I've said above, that's not a fact we can "derive from first principles". It's just something that's empirically been found to be true,

But it's notable that the first few layers of a neural net like the one we're showing here seem to pick out aspects of images (like edges of objects) that seem to be similar to ones we know are picked out by the first level of visual processing in brains.

So how does neural net training actually work? Essentially what we're always trying to do is to find weights that make the neural net successfully reproduce the examples we've given. And then we're relying on the neural net to "interpolate" (or "generalize") "between" these examples in a "reasonable" way.

There's nothing particularly "theoretically derived" about this neural net; it's just something that—back in 1998—was constructed as a piece of engineering, and found to work. (Of course, that's not much different from how we might describe our brains as having been produced through the process of biological evolution.)

And—as we'll discuss later—these weights are normally determined by "training" the neural net using machine learning from examples of the outputs we want.)

Ultimately, every neural net just corresponds to some overall mathematical function—though it may be messy to write out. For the example above, it would be: The neural net of ChatGPT also just corresponds to a mathematical function like this—but effectively with billions of terms.