A geometric approach to differential forms

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1. 6gvONxR4sf7oNov 2020
   I just looked through my bookshelves and here’s what I’ve been through since college (in no particular order other than top to bottom on my shelves):

   7 books on general ML (highlights: Murphy’s Machine Learning, Hastie et al’s ESL, Koller&Friedman’s PGMs)

   5 on more specialized ML (highlights: Agarwal&Chen’s statistical recommender systems book, Manning&Schutze’s statistical NLP, Settles’ active learning)

   13 on stats (highlights: Wooldridge’s econometrics of x-section/panel data, Angrist&Pischke’s econometrics)

   4 on numerical methods (highlight: Absil et al’s optimization on matrix manifolds)

   4 on CS (highlight: CLRS’s intro to algorithms)

   10 on calculus/geometry/topology/algebra (highlights: Bachman’s geometric approach to differential forms, Hestenes&Sobzyk's Clifford algebra to geometric calculus)

   8 on fiction writing (highlight: Bickham’s Scene & Structure)

   And Rosenberg‘s Nonviolent Communication (not a textbook, but still a highlight worth mentioning).

   It amounts to between 3 and 4 per year. Looking back and counting them up, my reaction is holy crap that’s a lot, but that’s kinda the point. Each year it is a reasonable amount of self study. Not nothing, but not anything crazy. Over the course of many years, it adds up to a hell of a lever.

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2. thraxilJun 2021
   "A Geometric Approach to Differential Forms" by Bachman is the one that's in my Amazon shopping cart, but I'll take a look at those others as well.
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