It’s been a lovely Christmas day. More social than any that I can remember, for a very long time. (Wonderful morning visit with my next-door neighbor. Great mid-day party. A sweet restorative nap afterwards.) And now, the thoughts that have been buzzing through and around my head for the past 48 hours — how to get started with deep learning. Of course there are all sorts of entry points. Historical, functional, mathematical… But what came to me, over these past…
Variational Free Energy I spent some time trying to figure out the derivation for the variational free energy, as expressed in some of Friston’s papers (see citations below). While I made an intuitive justification, I just found this derivation (Kokkinos; see the reference and link below): Other discussions about variational free energy: Whereas maximum a posteriori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution…