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You really find large n-dimensional transforms easier to reason about and visualise, as opposed to layers of neurons with connections? You don’t find it much m
by joefourier 5y ago
You really find large n-dimensional transforms easier to reason about and visualise, as opposed to layers of neurons with connections? You don’t find it much more intuitive to see it as a graph once you start adding recurrence, convolutions, sparsity, dropout, connections across multiple layers, etc., let alone coming up with new concepts?
I think it’s useful to understand it in both ways, but our intuitions about transforms are largely useless when the number of dimensions is high enough.
- nerdponx 5y agoIt's good to have both perspectives. Ideally you learn the layers-of-transforms version alongside the styled graph-of-neurons version. If you had to only pick one, which one you learn would depend a lot on what kind of student you are and what your goals are. I think the layers-of-transforms version is "less wrong" in general, but probably harder to understand, so it's maybe better if you had to learn just one.
- farresito 5y agoNot the person you are answering to, but I think it's all about the level of abstraction you want to reason at. I didn't grok neural networks until I visualized the transformations that were happening in a very simple network. Once that made sense, I could start thinking in terms of layers.
- ravi-delia 5y agoI think understanding how neural networks work is easiest if you think of them as networks. Reasoning about why they work is a lot easier thinking about them as transformations. It's not like you're actually picturing all the parameters of a nontrivial network one way or the other.