Subject: [Idnet] Getting going with ML

From: David Meyer <[email-masked]>
Date: Fri, 24 Mar 2017 06:54:25 -0700

Folks,

If you are just getting going with ML there is a wealth of material to help
you around the web. There are tons of tutorials, research papers, code,
videos, ... to help. In addition, over the past couple of years I've
written up tutorials for my teams as issues came up; see
http://www.1-4-5.net/~dmm/ml; there are some intro documents here and
pointers to the associated code (mostly but not all written in
tensorflow).  For example, if you are just getting going with Bayesian
inference see http://www.1-4-5.net/~dmm/ml/ps.pdf, "Notes on some basic
probability stuff",  and/or http://www.1-4-5.net/~dmm/ml/vae.pdf on
variational inference; variational inference is a form of approximate
inference that, among other things, attempts to deal with the intractable
integral in Bayes' Rule (the "evidence" aka "marginal likelihood") . There
are also great curated lists of current papers all around the net, see
e.g., https://github.com/terryum/awesome-deep-learning-papers (Most Cited
Deep Learning Papers).

To get going I also highly recommend Andrew Ng's Coursera ML course; most
of the material is on youtube, e.g,
https://www.youtube.com/watch?v=LLx4diIP83I (this one is on Logistic
Regression, but the whole set is there) if you want to skip the Coursera
overhead. Finally, there is a lot more material on
http://www.1-4-5.net/~dmm/vita.html (under Recent Talks) and in
https://github.com/davidmeyer/ml.

As always, questions/comments/* greatly appreciated.

--dmm