
The Matrix Cookbook (2012) [pdf] - nabla9
http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3274/pdf/imm3274.pdf
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imfletcher
I clicked wondering what kind of food they were making from the movies.

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fiatpandas
Must confess, before clicking my mind immediately went to "wonder if it
contains a recipe for the mush they ate on the nebuchadnezzar?"

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ivan_ah
Since we're on the topic of matrices and linear algebra, here is a tutorial on
the basics:
[https://minireference.com/static/tutorials/linear_algebra_in...](https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf)

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kcanini
This PDF literally got me through grad school. It's an amazing reference.

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refrigerator
Similar, but more "cheat-sheet" style:
[http://www.cs.nyu.edu/~roweis/notes/matrixid.pdf](http://www.cs.nyu.edu/~roweis/notes/matrixid.pdf)

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ivan_ah
This is an awesome resource that I keep coming back to again and again. Save
this somewhere on your computer so you'll have it handy whenever you see some
weird matrix derivative...

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highd
I used to use this a bunch when calculating gradient descent algorithms, but
I've since moved to using libraries with auto-differentiation (numpy with
autodiff or theano) and it's a huge force multiplier for my work (not to
mention GPU implementations basically for free).

Of course there's always some derivation that's easiest done by hand.

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netheril96
Isn't tensorflow the most popular auto differential library these days?

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gtani
Probably you could look at # Watches, Stars, forks etc to gauge that, but if
you want to look at vs theano, torch, pyTorch, Caffe2, start with cs231N
lecture:
[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8....](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf)

(I think there's vid on youtube)

