> Stripped of anything else, neural networks are compositions of differentiable primitives
I’m a sucker for statements like this. It almost feels philosophical, and makes the whole subject so much more comprehensible in only a single sentence.
I think François Chollet says something similar in his book on deep learning: one shouldn’t fall into the trap of anthropomorphising and mysticising models based on the ‘neural’ name; deep learning is simply the application of sequences of operations that are nonlinear (and hence capable of encoding arbitrary complexity) but nonetheless differentiable and so efficiently optimisable.
I’m a sucker for statements like this. It almost feels philosophical, and makes the whole subject so much more comprehensible in only a single sentence.
I think François Chollet says something similar in his book on deep learning: one shouldn’t fall into the trap of anthropomorphising and mysticising models based on the ‘neural’ name; deep learning is simply the application of sequences of operations that are nonlinear (and hence capable of encoding arbitrary complexity) but nonetheless differentiable and so efficiently optimisable.