I’m back form a conference, and will try to look into JuliaAcademy. I find three courses on machine learning: one advanced one using Flux, one intermediate on the math of machine learning, and one on Knet. Which one would you think is suitable for me? I’m not interested in “feature extraction” things initially.
(I.e., I’m not interested in image recognition, hand writing recognition, etc. at this stage, but rather on simple “least squares” mappings from real inputs to real outputs. I know this can be done by choosing c_i such that y = \sum_i c_i \phi_i(x) + e where \phi_i(x) are chosen basis functions and e is some model error, and using simple linear algebra, but I’m interested in first understanding how this can be solved by “chaining” linear-combination + nonlinear output mapping layers a machine learning tool.)