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=== Stat Mech and Stat Inference === * [https://libgen.unblocked.pub/book/index.php?md5=4CFDD37EEDB946D6E944750F746DB72B Bishop - Pattern Recognition and Machine Learning] Great pedagogical introduction to the basics. Good treatment of exponential family. * [http://www.inference.org.uk/itila/p0.html MacKay - Information Theory, Inference, and Learning Algorithms] Link [https://libgen.unblocked.srl/book/index.php?md5=E70CC484C7FF51073859B15779162C25 here] * [https://libgen.unblocked.srl/book/index.php?md5=E4B2AB0EF22458F94C835D4D2397034E Goodfellow et al. - Deep Learning] * [https://libgen.unblocked.srl/book/index.php?md5=3E76F8F5189A047550CA9020D97848E4 Mezard - Information, Physics, and Computation] * [https://libgen.unblocked.srl/book/index.php?md5=AFEE8F3BEF62DC5C7A191451259AD8EB Engel - Statistical Mechanics of Learning] I haven't looked at this yet, but it seems promising. * [https://libgen.unblocked.srl/book/index.php?md5=0AF108CB2FEFA5A20F7B186BC2C88656 Jaynes - Probability Theory, The Logic of Science] An excellent introduction to Bayesian reasoning and probability theory in general, from a very pedagogical, opinionated point-of-view. Dives into the motivations behind some information theory as well.
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