physicsguy2

Completely agree! It feels like a gimmick to make things sound fancier than they are.

A key property of (actual) tensors is how they transform under coordinate transformations. One can store the components in multi-dimensional arrays but that doesn't fully describe the tensor.

Vectors which are a kind of tensor (of type (1,0) i.e. maps from the dual space to the field) also transform in a very certain way under coordinate (change of basis) transformations.

In physics, every quantity has certain transformation properties under rotations, Lorentz transformations, internal symmetry transformations and that puts a tight constraint on the quantities that can be constructed and the "tensorness" or "vectorness" of quantities has deep meaning. The use in deep learning completely bypasses all the meaning and instead defines a tensor to be a high-dimensional matrix.

Check out the number of top athletes who are vegetarian or vegan. Go to a typical Spartan race in your state and you'll find a ton of top performers who don't eat meat. If you are in the US, there are enough healthy alternatives even outside the two coasts.

Also, this choice doesn't have to be binary. One could eat less meat - either smaller quantities or fewer meals with meat each week. The end goal is simple - reduce the number of animals who have to be raised and killed for meat each year. If one agrees on that (and one might not), then even reducing your intake by 10% helps a lot.

>> You also can’t force societies values to change around you

I see your point but would you advocate the same stance for other ethical issues - refusal of restaurants to serve African Americans before the civil rights movement?

Having said that, I agree that I don't like the sense of entitlement many people have in restaurants where they demand to be served something the restaurant clearly doesn't usually provide. But I do hope these "abstraction layers" of animal raising, slaughter, meat cleaning and packing and then buying a clean, small pack from a local store get "leaky". People need to see what other species go through for that meal on our tables.

I find this a bit funny. You are comparing books that are used by advanced undergraduates or graduate students to a book written essentially for the subset of programmers with almost no math background. There are beautiful mathematics books by Springer (Undergraduate Texts in Mathematics), Carus Mathematical Monographs and Dover that provide both intuition and rigor at an introductory level. The main problem for a beginner though is knowing where to start and getting a survey of the arena they are entering. They might think linear algebra is adding vectors together with a vector being just a list of numbers.

This book hopefully fills that gap. There are other introductory books like Mathematics: Its Content, Methods & Meaning by Aleksandrov and Kolmogorov but that requires a serious investment of time.

Lastly, I apologize but I am going to rant about this physicist phenomenon. I am a physicist myself (high-energy theory) that entered quantitative finance after my PhD (a heavily disliked field on HN). I found a lot of physicists outside academia who tend to be aggressive and condescending and often haven't actually gone through the rigors of a PhD program themselves. Maybe it makes one feel smarter to tell other people that they are reading easy books or can't jump straight to Landau or Bourbaki (those two couldn't be more different but you get the point) and there are no other physicists around to correct the notion that there are multiple ways to gain knowledge and build intuition.

It doesn't matter who says what about which book. Sample some books, find what you like as long as its not junk science/mathematics, do plenty of exercises, and try to derive things yourself and don't fool yourself that you understand something deeply by studying it for a year.