In a graduate numerical optimization class I took over a decade ago, the professor spent 10 minutes on the first day deriving some matrix calculus identity by working out the expressions for partial derivatives using simple calculus rules and a lot of manual labor. Then, as the class was winding up, he joked and said "just kidding, don't do that... here's how we can do this with a Taylor expansion", and proceeded to derive the same identity in what felt like 30 seconds.

Also, don't forget the Jacobian and gradient aren't the same thing!

Every gradient is a Jacobian but not every Jacobian is a gradient.

If you have a map f from R^n to R^m then the Jacobian at a point x is an m x n matrix which linearly approximates f at x. If m = 1 (namely if f is a scalar function) then the Jacobian is exactly the gradient.

If you already know about gradients (e.g. from physics or ML) and can't quite wrap your head around the Jacobian, the following might help (it's how I first got to understand Jacobians better):

1. write your function f from R^n to R^m as m scalar functions f_1, ..., f_m, namely f(x) = (f_1(x), ..., f_m(x))

2. take the gradient of f_i for each i

3. make an m x n matrix where the i-th row is the gradient of f_i

The matrix you build in step 3 is precisely the Jacobian. This is obvious if you know the definition and it's not a mathematically remarkable fact but for me at least it was useful to demystify the whole thing.

This would be nice, because it would generalize the "gradient" from vector calculus, which is clearly and unambiguously a vector.

I'm a simple-minded physicist. I just know if you apply the same coordinate transformation to the gradient and to the displacement vector, you get the wrong answer.

My usual reference is Schutz's Geometrical Methods of Mathematical Physics, and he defines the gradient as df, but other sources call that the "differential" and say the gradient is what you get if you use the metric to raise the indices of df.

But that raised-index gradient (i.e. g(df)), is weird and non-physical. It doesn't behave properly under coordinate transformations. So I'm not sure why folks use that definition.

You can see difference by looking at the differential in polar coordinates. If you have f=x+y, then df=dx+dy=(cos th + sin th)dr + r(cos th - sin th)d th. If you pretend this is instead a vector and transform it, you'd get "df"=(cos th + sin th)dr + (1/r)(cos th - sin th)d th, which just gives the wrong answer.

To be specific, if v=(1,1) in cartesian (ex,ey), then df(v)=2. But (1,1) in cartesian is (1,1/r) in polar (er, etheta). The "proper" df still gives 2, but the "weird metric one" gives 1+1/r^2, since you get the 1/r factor twice, instead of a 1/r and a balancing r.

Let's say I want to differentiate tr(X^T X), tr is the trace, X is a matrix, and X^T is its transpose. Expand:

    tr((X + dX)^T (X + dX)) = tr(X^T X) + 2 tr(X^T dX) + tr(dX^T dX).

  https://math.stackexchange.com/questions/3680708/what-is-the-difference-between-the-jacobian-hessian-and-the-gradient

  https://carmencincotti.com/2022-08-15/the-jacobian-vs-the-hessian-vs-the-gradient/

https://sassafras13.github.io/MatrixExps/

“Thanks to a fabulous video by 3Blue1Brown [1], I am going to present some of the basic concepts behind matrix exponentials and why they are useful in robotics when we are writing down the kinematics and dynamics of a robot.”

  >  Note that you do not need to understand this material before you start learning to train and use deep learning in practice

  > You don't need to know math to train good models, but you do need to know math to know why your models are wrong. 

I think another part is that the Bright, Edleman, and Johnson paper are also introducing concepts such as Automatic Differentiation, Root Finding, Finite Difference Methods, and ODEs. With that in mind it is far more important to be coming from the approach where you are understanding structures.

I think there is an odd pushback against math in the ML world (I'm a ML researcher). Mostly because it is hard and there's a lot of success you can gain without it. But I don't think that should discourage people from learning math. And frankly, the math is extremely useful. If we're ever going to understand these models we're going to need to do a fuck ton more math. So best to get started sooner than later (if that's anyone's personal goal anyways)

edit: On the math side I've encountered one that seemed unique, as I haven't seen anything like this elsewhere: https://irregular-rhomboid.github.io/2022/12/07/applied-math.... However, this only points out courses that he enrolled in his math education that he thinks is relevant to ML, each course is given a very short description and or motivation as to the usefulness it has to ML.

I like this concluding remarks:

Through my curriculum, I learned about a broad variety of subjects that provide useful ideas and intuitions when applied to ML. Arguably the most valuable thing I got out of it is a rough map of mathematics that I can use to navigate and learn more advanced topics on my own.

Having already been exposed to these ideas, I wasn’t confused when I encountered them in ML papers. Rather, I could leverage them to get intuition about the ML part.

Strictly speaking, the only math that is actually needed for ML is real analysis, linear algebra, probability and optimization. And even there, your mileage may vary. Everything else is helpful, because it provides additional language and intuition. But if you’re trying to tackle hard problems like alignment or actually getting a grasp on what large neural nets actually do, you need all the intuition you can get. If you’re already confused about the simple cases, you have no hope of deconfusing the complex ones.

https://irregular-rhomboid.github.io/2022/12/27/math-is-a-la...

https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf

I wouldn't teach it this way, but I would definitely take the taylor expansion and define the grad vector as the one to make the best local linear approximation. This tells you that the grad lives in the same space, i.e. same dimensions.

Of course, you can always switch things around if you want to calculate things and put in transposes where they belong. But I find it insane to take the unnatural view point as standard, which I find a lot of papers do.

So in this particular instance since we are talking about matrix calculus it’s a type of multivariable calculus where you’re dealing with functions which take matrices as inputs and “matrix-valued functions” (ie functions which return matrices as an output).

Calculus is used for a lot of things but for example if you have a continuous function you can use calculus to find maxima and minima, inflection points etc. Since the focus here is machine learning, one of the most important things you want to be able to do is gradient descent to optimise some cost function by tweaking the weights on your model. The gradient here is a vector field which at every point in the space of your model points in the direction of steepest ascent[1]. So if you want to go down you take the gradient at a particular point and go exactly in the opposite direction. That means you know exactly what model weight tweaks will cause the biggest decrease in your cost function the next time you do a training run.

[1] To imagine the gradient, think of an old-school contour map like you’re going to do a hike or something. This is a “scalar field” (a map from spatial coordinates to a scalar value - altitude). The contour lines on the map link points of equal altitude. The gradient is a “vector field” which is a map from spatial coordinates to a vector. So imagine at every point on your contour map there was a little arrow that pointed in the direction of steepest ascent. Because this is matrix calculus you will be dealing with “matrix fields” (maps from spatial coordinates to matrices) as well. So for example say you did a measurement of stress in a steel beam. At each point you would have a “stress tensor” which says what the forces are at that point and in which direction they are pointing. This is a “tensor field” (map from spatial coordinates to a tensor) and a tensor is like a multidimensional matrix but with some additional rules about how it transforms.

Though Steven Johnson is the real deal and writes lots of code, Edelman is a shyster/imposter who used to ride the coattails of G. Strang and now shills for Julia where he makes most of his money. You don't need, and won't understand ML/LLM by reading textbooks.

1. If you want to have a little fun with ML/LLM, fire up Google Collab and run one of tutorials on the web - Karpathy, Hugging Face or PyTorch examples.

2. If you don't want to do, but just read for fun, Howard & Parr's essay as recommended by someone else here is much shorter and more succinct. https://explained.ai/matrix-calculus/ this link renders better

3. If you insist on academic textbooks, Boyd & Vandenberghe skips calculus and has more applications (engineering). Unfortunately, code examples are in Julia! https://web.stanford.edu/~boyd/vmls/vmls.pdf https://web.stanford.edu/~boyd/vmls/. link to python version

4. If you Want to become a tensor & differential programming ninja, learn Jax, XLA https://docs.jax.dev/en/latest/quickstart.html https://colab.research.google.com/github/exoplanet-dev/jaxop...