same thing with any feed forward network too. They are all piece-wise linear in respect to inputs. Layers reduce resource requirements and make some patterns e

same thing with any feed forward network too. They are all piece-wise linear in respect to inputs. Layers reduce resource requirements and make some patterns easier or even practical to find, but any ANN that is a FNN supervised learning could be represented as a parametric linear regression. Unsupervised learning, that tends to use clustering is harder to visualize but is the same thing. You still have ANNs, which have binary output, which can be viewed through the lens of deciders. They have to have unique successor and predecessor functions. Really this is just set shattering that relates to a finite VC dimensionality being required for something to be PAC learnable. But the title of this is confusing the map for the territory. It isn't that 'Everything is a linear model' but that linear models are the preferred, most practical form. The efforts to leverage spikey neutral networks, which is a more realistic model of cortical neurons, and which have continuous output (or more correctly the computable reals) tend to run into problems like riddled basins. https://arxiv.org/abs/1711.02160 https://arxiv.org/abs/1711.02160 Obviously setting rectified linear unit at 0 = 1 resolves to differentiation problem, but many functions may not be so simple Perhaps a useful lens is how TSP with a discreet Euclidean metric is in NP-complete while the continuous version is in NP-hard. But it isn't that everything is linearizable, but rather that linearized problems tend to be the most practical.