One very interesting thing about automatic differentiation is that you can think of it as involving a new algebra, similar to the complex numbers, where we adjoin an extra element to the reals to form a plane. This new algebra is called the ring of "dual numbers." The difference is that instead of adding a new element "i" with i² = -1, we add one called "h" with h² = 0!

Every element in the dual numbers is of the form a + bh, and in fact the entire ring can be turned into a totally ordered ring in a very natural way: simply declare h < r for any real r > 0. In essence, we are saying h is an infinitesimal - so small that its square is 0. So we have a non-Archimedean ring with infinitesimals - the smallest such ring extending the real numbers.

Why is this so important? Well, if you have some function f which can be extended to the dual number plane - which many can, similar to the complex plane - we have

f(x+h) = f(x) + f'(x)h

Which is little more than restating the usual definition of the derivative: f'(x) = (f(x+h) - f(x))/h

For instance, suppose we have f(x) = 2x² - 3x + 1, then

f(x+h) = 2(x+h)² - 3(x+h) + 1 = 2(x² + 2xh + h²) - 3(x+h) + 1 = (2x² - 3x + 1) + (4x - 3)h

Where the last step just involves rearranging terms and canceling out the h² = 0 term. Note that the expression for the derivative we get, (4x-3), is correct, and magically computed itself straight from the properties of the algebra.

In short, just like creating i² = -1 revolutionized algebra, setting h² = 0 revolutionizes calculus. Most autodiff packages (such as Pytorch) use something not much more advanced than this, although there are optimizations to speed it up (e.g. reverse mode diff).

Dual numbers implement forward mode automatic differentiation, but is there additional value to viewing AD in terms of duals e.g. when we’re implementing reverse mode (backprop)?

Depends what you mean by "additional value". Dual numbers are very simple, and enough for reverse mode AD too though, even via a purely functional implementation:

Provably Correct, Asymptotically Efficient, Higher-Order Reverse-Mode Automatic Differentiation, https://dl.acm.org/doi/pdf/10.1145/3498710

This is a good question that doesn't have a short answer. There are some different philosophical opinions about this.

One way to look at this is to note that even with forward mode autodiff, there have generally historically been two different viewpoints for the whole thing, which I'll call the "computer science" view and the "algebraic" view.

The computer science view involves things that look computational graphs with nodes that look like {"value": 123, "deriv": 456}. We are storing real values along with propagated partial derivatives. We have a custom * operator which sets a * b = {"value": a.value * b.value, "deriv": a.value * b.deriv + b.value * a.deriv}. Other functions, like exp, sin, cos, log, etc are also extended to handle these kinds of input. There's a ton of literature that views things using this kind of framework going back to the 1960s.

The algebraic view uses dual numbers. Instead of writing {"value": 123, "deriv": 456}, we write 123 + 456h. We get the same results as the above: (a + bh) * (c + dh) = ac + (ad + bc)h. We can extend many functions in a natural way to the dual plane, such as exp, sin, cos, log, and get values there. There's also plenty of literature on these, going back to the late 1800s.

A modern view is to note that these two things are *identical*. It isn't that forward mode autodiff "uses" dual numbers; it is dual numbers. The set of elements of the form {"value": x, "deriv": y}, with addition and multiplication as stated, satisfies the axioms of a real algebra and is isomorphic to the dual numbers. We could have written {"real_part": x, "dual_part": y} if we wanted. You can see this viewpoint in some of the links I've posted elsewhere here.^[1]

So given all of that background, there are two answers to your original question. The first is to just simply view it as that "reverse-mode autodiff doesn't use dual numbers." Many people have this view, and I would say that that it really focuses on what I've called the "computer science" view above.

The second view is to note that the relationship between reverse-mode autodiff and dual numbers is the same as the relationship between reverse-mode autodiff and forward-mode autodiff. It would be silly to say that they are totally different, unrelated things: at the end of the day all we are really doing is changing the order in which we perform a bunch of multiplies of Jacobian matrices. I tend to view it as similar to the relationship between the DFT and the FFT: there is this super elegant linear algebra view involving DFT matrices. Do we say that the FFT "doesn't use matrices?" Well, I guess, but are we going to go so far as to say that it also doesn't involve linear algebra, etc? That is my view.

There are a few other differences between reverse-mode and forward-mode autodiff. Each individual operation in the computation graph, for instance, can be thought of as an individual instance of forward-mode autodiff. In reverse-mode, on the other hand, we typically store the (back-)propagated adjoints as additional data on the input node objects, not the output nodes. This is useful if we are thinking of backpropagation on a graph. It's up to you if you view these as involving materially different theories or just differences of implementation for the sake of optimization.

In short, the main thing is that there's less literature on a purely algebraic version of reverse-mode autodiff in general.

[1] There is one important difference: we often think of the dual numbers as an ordered ring with "h" infinitesimal. The first viewpoint doesn't use this part of the mathematical structure - and it's very interesting to note that it isn't even necessary! Although it's cute to think of h as infinitesimal, the autodiff properties we get flow purely from the algebraic properties of having h^2 = 0, regardless of order. Of course, though, we can always just think of the dual numbers as an unordered ring, if we want.

Where do I go to learn what you just said?

There are a few good resources. Here is a good blog post with some introduction:

https://towardsdatascience.com/forward-mode-automatic-differ...

Some thorough notes from MIT: https://book.sciml.ai/notes/08-Forward-Mode_Automatic_Differ...

Here is a teacher who taught a class using these, calling the dual element dx instead of h:

https://cornellmath.wordpress.com/2007/08/28/non-nonstandard...

The book referred to in this post has some information about this. The method with "dual numbers" is called "forward-mode automatic differentiation". PyTorch seems to use "reverse-mode automatic differentiation", which does not use dual numbers, but keeps track of the computation graph.

you guys are wrong and spreading blatant misinformation - there is no magic number whose square is 0 but which is itself not zero anywhere in pytorch or tensorflow or any other real DNN framework that i'm familiar with. it's all fun and games to participate in math woo but you shouldn't be proclaiming things you don't actually know on a public forum.

The dual numbers exist just as surely as the real numbers and have been used well over 100 years

https://en.m.wikipedia.org/wiki/Dual_number

Pytorch has had them for many years.

https://pytorch.org/docs/stable/generated/torch.autograd.for...

JAX implements them and uses them exactly as stated in this thread.

https://github.com/google/jax/discussions/10157#discussionco...

Many other frameworks use them also, for many reasons.

As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.

They exist by definition. Your claim makes no more sense than confidently proclaiming that there is no x such that x^2 < 0. We invented the imaginary numbers and made it so. So too the dual numbers.

I don't know why you keep calling it "magic". Whether or not pytorch uses them, they aren't magic, neither in the derogatory sense nor in the praise sense.

> I don't know why you keep calling it "magic".

because they have all of the gee-whiz factor of a freshman calc proof of the chain rule that divides and multiplies infinitesmals and absolutely not enough of the substance necessary to prove much more than that. they are absolutely, in the research literature, at best an anachronism (harkening back to leibniz) and at worst a parlor trick.

in literally my first response i provided the most trivial counter-example to the magic of non-standard analysis. no answers (crickets). i surmise this is because the people in here talking it up aren't really serious.

As I use the terms, the dual numbers are a different thing from non-standard analysis. Non-standard analysis, as I understand the term, uses non-standard models of the real numbers, and its infinitesimals do not satisfy h^2 = 0. In non-standard analysis, f'(x) is the standard part of (f(x+h)-f(x))/h , for an infinitesimal h (i.e. for a non-standard real which is smaller than any non-zero standard rational number). (In order to apply this definition, f should be defined in a way which does not use anything requiring determining if a number is standard, or taking the standard part of something, etc.)

The dual numbers, on the other hand, are the ring R[h]/(h^2) . This is not a field, while non-standard models of the real numbers do form fields.

The dual numbers suffice to define differentiation of polynomials (which may not be sufficient for some purposes! [a]), and something like dual numbers is used in algebraic geometry to define the Zariski tangent spaces for points of algebraic varieties (whether in characteristic 0 or in characteristic p. In characteristic p, one certainly can't use an epsilon-delta definition!).

I really don't see your point about the "gee-whiz factor". While different things, both non-standard analysis and dual numbers can be handled rigorously, and have their use-cases, even though I certainly would at least default to thinking of differentiation in terms of the limits definition (assuming I'm thinking of any specific definition at all).

I assume that the counter-example you refer to is the (dx/dy)(dy/dz)(dz/dx) thing. That indeed doesn't seem like the kind of thing that using the dual numbers would be especially suited for. Though, also, not the sort of thing that should really come up in auto-diff I would think?

If there is a common parameterization of the values of x,y,z by some variable t (on some interval I), on some neighborhood of the point under consideration, where {(x(t),y(t)) | t in I}, {(y(t),z(t)) | t in I}, and {(z(t),x(t)) | t in I}, are each differentiable functions, and where they satisfy the relationships between the variables x,y,z from the larger context, and all three of (dx/dy), (dy/dz), (dz/dx) exist at the point in question, then it seems that the product should be 1.

[a] Though, if the function is analytic (not just smooth), unless I'm missing something, it should also give the right answer (but still not a good definition of course, because should define differentiation before defining what it is for a function to be analytic.) (of course, just because the function is analytic doesn't make its domain include the dual numbers. One has to take a power series for it, and apply this power series to the element of the ring of dual numbers, not apply the original function to it.)

The grandparent I'm responding to sure uses a very sloppy presentation of things. Not everyone here is a trained mathematician though, so you may want to give people some slack.

Obviously, if h² = 0, then h = 0, so this statement made no sense. What the author probably tried to convey, is that one can reason with infinitely small values as symbols, and perform automatic differentiation with that.

No, there’s an abstract algebra extension of real numbers to have an extra symbol h such that h^2=0. This is not a real number so you cannot apply the argument h^2=0 implies h=0, much like complex numbers don’t obey all properties of real numbers.

(For example for real numbers, x!=0 implies x^2>0 but i^2=-1)

https://en.m.wikipedia.org/wiki/Grassmann_number

  a^2 = 1, first base vector is a regular one
  b^2 = -1, second base vector is "imaginary"
  ab = 0, base vectors are orthogonal

  (a+b)^2 = a^2 + 2ab + b^2 = 1 + 2\*0 + (-1) = 0

Trick is taken from conformal geometric algebra [1].

[1] https://en.wikipedia.org/wiki/Conformal_geometric_algebra

Your are confusing non-standard and dual numbers. The dual numbers are not ordered and contain non-invertible nilpotent elements such as h which squares to 0.

These things are different and I did mean dual numbers. The dual numbers do form an ordered ring. When you complain about certain elements being non-invertible, I think you are probably complaining that they aren't a field.

If a < b then a^2 < b^2. This is not true if you let a = 0 and b = h. The dual numbers do not have an ordering. You should provide sources and proofs next time because it seems like you are just making things up.

That isn't what an ordered ring is. Your property of a < b → a² < b² doesn't even hold true in the integers. For instance, let a = -2 and b = -1.

The correct property is that if a ≤ b, a + c ≤ b + c, and if a ≥ 0 and b ≥ 0, then ab ≥ 0. It is fairly easy to see that these properties hold for dual numbers.

In my argument a and b are positive and this is true for all ordered rings but not for the dual numbers as you've defined them. Specify the ordering and you will realize h can not be larger nor smaller than 0 because both cases lead to a contradiction.

In any case, I'm dropping out of this thread.

It isn't true for all ordered rings, and the dual numbers are in fact a counterexample to the claim that it is true.

Beyond that I'm not sure what to tell you, other than it's fairly easy to see that the dual numbers do satisfy the axioms of an ordered ring that I gave. Here's a large survey of various infinitesimal systems by Philip Ehrlich where he also notes the dual numbers are an ordered ring: https://arxiv.org/pdf/1808.03345.pdf.

> Most autodiff packages (such as Pytorch) use something not much more advanced than this

pytorch absolutely does not use the dual number formulation - there are absolutely no magic epsilons anywhere in pytorch's (or tensorflow's) code base. what you're calling duals are the adjoints where are indeed stored/cached on every node in pytorch graphs.

there's a reason no one uses dual numbers (non-standard analysis) for anything (neither autodiff nor calculus itself): because manipulating infinitesmals like this is fraught formal manipulation (it's algebra...) where as limits are much more rigorous (bounds, inequalities, convergence, etc.). my favorite question to ask the non-standard analysis n00bs is: please tell me under what conditions this is true

(dx/dy)(dy/dz)(dz/dx) = 1

edit:

anyone that thinks i'm wrong and this other guy is right should go and do some reading, eg where this guy tried to make this same point and got shot down:

https://math.stackexchange.com/a/341550

spoiler alert: there's a reason you had to learn epsilon-delta proofs and limits and it's not because your math professors are mean.

this is why i hate this kind of "TIL, gee whiz" math tidbits - they're full of exclamation marks and fancy sounding words ("non-archimedean rings" oooo fancy) but almost always come from a wikipedia level understanding, not actual research.

At the end of the day, if you are storing inputs and outputs to a function as a pair of numbers - one for the actual value, and one for the derivative - and if addition and multiplication work the way you expect and propagate derivatives correctly - then you are using dual numbers, regardless of if you notate it a + b*h or {"value": a, "derivative": b}.

Pytorch does things slightly differently in that it is mostly focused on reverse-mode autodiff, and so it stores adjoints relative to the overall output rather than partial derivatives relative to the input, but this isn't really an entirely different thing, in the same way that the FFT isn't entirely different from the DFT.

There seems to be some confusion about the relationship between dual numbers and smooth infinitesimal analysis. Both have nilpotent elements, but with dual numbers the background logic is classical, whereas it isn't with smooth infinitesimal analysis.

EDIT: I see you've edited your post to try to get in some extra criticism after I've already responded. That's terrible form, so I'll just respond here.

Dual numbers are a nice way to get started with forward-mode autodiff, to which it is so related that the two are essentially the same thing with different labels. Pytorch instead uses reverse-mode autodiff. Reverse-mode and forward-mode autodiff are different, but not so different that they are entirely different things. Reverse-mode is, as I put it in my OP, "not much more advanced" than forward-mode, even if not identical.

What is entirely different, much more advanced, and what Pytorch really doesn't do, is anything like the "epsilon-delta proofs" you keep hanging your hat on. If Pytorch did that, it would be useless. The entire point of autodiff is to avoid such things.

Beyond that, I would suggest slowing down a bit as you are mixing quite a few things up. Nonstandard analysis has nothing to do with dual numbers at all, for instance. And you're very much misinterpreting that MSE post of mine you linked to (thanks!).

> and if addition and multiplication work the way you expect and propagate derivatives correctly - then you are using dual numbers

you literally started out your miraculous comment with

> This new algebra is called the ring of "dual numbers." The difference is that instead of adding a new element "i" with i² = -1, we add one called "h" with h² = 0!

not some observation about caching derivatives.

so i'll repeat myself for the 3rd time: there are no magical numbers anywhere in pytorch or tensorflow or cafe or any other serious autodiff implementation that abide by the rules you so jubilantly exclaim about.

See my comment to Mike. I think you're making a valid point here, which is that dual numbers by themselves are not powerful enough to automatically generate derivatives of arbitrary functions, especially given that those functions could be implemented in a FPU core, or using methods like lookup tables that don't lend themselves to dual number differentiation.

Dual numbers help us automatically differentiate things when the functions themselves are implemented as analytic power series that we have to explicitly compute without accelerator help. In such cases we can indeed use them. But to your point, serious forward AD engines need to differentiate functions that are computed in one shot by accelerator hardware.

However Mike makes a very valid counterpoint when he shows forward mode AD in Torch. I believe a careful analysis of Torch's implementation here could bring this conversation to a productive and satisfying conclusion for all participants and our public audience.

My big question here is to what degree did the implementers try to respect the dual number approach? Did they implement a dual tensor class for instance? Do they automatically lift some ordinary computations into dual tensor computations? I honestly have my doubts there.

I have confidence that we can get to the bottom of this. I think that Mike actually does care about automatic differentiation, and would be receptive to discussing this point of subtlety that naive dual number implementations may not be enough for industrial strength AD systems, with clear examples of code and clear reasoning as to how dual numbers fail in important cases.

Thank you for repeating yourself three times. It seems like you think that the dual number algebra involves "magic woo numbers." It seems like you haven't really worked through this stuff too much. I would suggest reading some of the resources above, such as the MIT lecture series. The rest of your points I think I have already addressed, though you ignored in your reply - I've said Pytorch does reverse mode diff several times at this point.

> It seems like you haven't really worked through this stuff too much

yup not at all - i just wandered in off the street and knew accidentally that you were talking about non-standard analysis.

> The rest of your points I think I have already addressed

please show me the source line number in pytorch or tensorflow that defines this number

> we add one called "h" with h² = 0!

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

This could help settle the objection that torch doesn't implement dual number based Forward Accumulation.

But I'm wondering if it does it by implementing dual tensors and automatically 'lifting' ordinary tensor computations into dual tensor computations? That would be a little surprising to me.

The more common approach I have seen is that we decorate existing operations with additional logic to accumulate and pass on a derivative value as well as the actual value during evaluation. This can be important for instance for transcendental functions, which might be computed with methods like lookup tables and approximate series, which do not necessarily lend themselves to accurate dual number computations, but do have a straightforward formulas for the derivative. It can also be a requirement when our transcendentals are computed in the FPU, which does not expose any power series to automatically thread dual our numbers through.

It would make sense in the case of something like pytorch if this were the case, since it could be a bit of a stretch to expect the correct numbers to appear if only we just compute everything with dual numbers. Indeed, the original torch functions certainly exploit the FPU, so we very likely have to explicitly formulate a derivative in at least some cases.

I wonder if this observation could help heal the rift between the two positions here - it seems like your counterpart could be satisfied with the view that most forward mode AD it's not quite as "pat" as just injecting a dual numbers library into existing code, but requires careful extension to accurately accumulate the derivatives of each operation in the system.

I believe that reaching common ground around that fact could help your counterpart reach a satisfying conclusion here. The methods are clearly dual number in spirit, but may require more subtle implementation details then the traditional dual number story, which states "dual numbers get you free derivatives with no need to extend your functions". Pointing out how this does break down for general functions is not only true, but could serve as an olive branch and opportunity to advance this discussion.

Besides that remark, which I made in the intent of resolving a conflict and potentially fostering communication, I wanted to thank you for this amazing link that you shared. I did not know that pytorch had forward mode AD! I may just have to dig into it and see how they pull it off!

You seem somewhat obsessed with the idea that reverse-mode autodiff is not the same technique as forward-mode autodiff. It makes you,,, angry? Seems like such a trivial thing to act a complete fool over.

What's up with that?

Anyway, here's a forward differentiation package with a file that might interest you

https://github.com/JuliaDiff/ForwardDiff.jl/blob/master/src/...

I can't reply to the guy saying julia is the only one. But there are others.

Ceres uses dual numbers

https://github.com/ceres-solver/ceres-solver/blob/master/inc...

This library from google is used everywhere in robotics, so it's hardly some backwater little side project.

So does c++ autodiff https://github.com/autodiff/autodiff/blob/main/autodiff/forw...

So does Eigen: https://eigen.tuxfamily.org/dox/unsupported/AutoDiffScalar_8...

So does Pytorch: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

it's amazing to me that pointing out a straight up mathematically factual inaccuracy is considered "angry" and "acting a fool".

> there's a reason no one uses dual numbers (non-standard analysis) for anything (neither autodiff nor calculus itself)

wrong

> there are no [dual] numbers anywhere in [...] any other serious autodiff implementation

wrong

> please show me the source line number

did

> i'm wrong and this other guy is right

correct.

> this is why i hate this kind of "TIL, gee whiz" math tidbits

acting a fool. angrily so.

is this like some kind of version of truman show? the full sentence is

>please show me the source line number in pytorch or tensorflow that defines this number

why? because the original comment makes a claim about pytorch. it's all right there in black and white.

Which claim, this one?

> Most autodiff packages (such as Pytorch) use something not much more advanced than this, although there are optimizations to speed it up (e.g. reverse mode diff).

We get it: you're a mechanic who thinks that makes you an automotive engineer.

i haven't a clue what that means but when julia's autodiff is the only one that is implemented using dual numbers i think the claim is pretty obviously false and seemingly everyone has an issue admitting that. without a doubt you'll just say something else sarcastic rather than admit it.

You've filled this page with comments on non-standard analysis, but the dual numbers have precisely zero to do with it. Calling people n00bs on a topic you apparently do no understand is silly.

Non-standard analysis deals with fields only, and the dual numbers are not a field, there are infinitely many zero divisors.

You should read the wiki pages on both, then maybe this mathoverflow post explaining it. The clearest way to maybe grasp the difference for you is that in any formulation of non-standard analysis, the square of any infinitesimal is another infinitesimal, and never 0. In the dual numbers, the square of any infinitesimal is always precisely, exactly zero.

They are so fundamentally different that anyone (like you) that claims to be so cognizant of either would never repeat they are the same as loudly and frequently as you are.

https://math.stackexchange.com/questions/341535/is-the-theor...

You're right on one count: no library implements forward mode. Hence, you're correct that no autodiff library (including pytorch) implement autodiff this way.

However, *you're wrong* that forward mode cannot be written in terms of dual numbers. The point is that the addition and multiplication operation for dual numbers correspond exactly to rules of the derivative of addition and the derivative of the product.

i didn't say it cannot be, i said there's a natural reason it's not: the same reason the rest of analysis isn't.

> You're right on one count: no library implements forward mode

there are plenty of CFD type libraries that implement forward mode. they also do not use what i'm calling "magical" dual numbers i.e. these nilpotent ring elements.

Nilpotents aren't exactly new.

This is amazing! Seems like a perfect excuse to get back into Julia. I just wish Julia had more compile targets. Ideally I'd like to have the option to target the browser (wasm/webgpu).

The Julia community seems like they've been out front when it comes to differentiable programming. For example: https://www.youtube.com/watch?v=rF2QAJLM730

Awesome, I hope it pans out

We have a lot of things compiling with web assembly, like the ODE solvers. See https://tshort.github.io/WebAssemblyCompiler.jl/stable/examp...

Would this be useful for general applications, or just numerical ones?

Your program takes various inputs, does processing and gives you results. To quote the book this applies to programs "including those with control flows and data structures" and are not entirely numerical in the most common sense. Broadly speaking, with AD you can modify the results and the program will spit out the required inputs to get those results.

So if a program's result is "no access", then AD can figure out how you can get access. Sounds like an important hacker tool.

Yes, these are “adversarial patches” in image classification, like https://arxiv.org/abs/1712.09665 . Similarly you can take these adversaries and add them to your own larger model, in an arms race.

Cryptography functions, I'd assume, are purposely non-differentiable.

I mean, “differential cryptanalysis” doesn’t go through AD, but I would be surprised if it’s not possible to get pretty close to a discrete analogue to AD using bit flips instead of differential and abstract interpretation.

> Broadly speaking, with AD you can modify the results and the program will spit out the required inputs to get those results.

I assume you're trying to phrase it in a non-technical way for accessibility, but I wonder if that might have lost some precision. What you describe sounds more like (logically) reversible programming (https://en.wikipedia.org/wiki/Reversible_computing). Differentiability doesn't imply reversibility; for example, the program that takes in an input and returns 1 is as differentiable as they come, but there's nothing that differentiation, automatic or otherwise, can do to tell you an input that will make it return 2.

But it can immediately return "no".

Good point! (And it brings back memories of fun with Prolog, although I guess that's `false`.)

This looks like a great resource! Differentiable programming is such a cool area. I'll never forget writing a differentiable PID controller a few years ago and watching the PID gains get tuned automagically to stabilize the control system. It's powerful stuff if you use it in the right places.

How did you do it?

Is there a resource explaining the process online? If there isn't, and you're willing to create one, I offer reviewing.

At Collimator.ai we built a differentiable modeling and simulation platform for dynamical systems on JAX (think Simulink but in the cloud and with Python+JAX instead of Matlab) in order to enable more efficient optimization, autotuning, MPC, neural network control, etc. Here is a simple example of PID tuning with JAX and autodiff: https://py.collimator.ai/examples/pid_tuning/ (feedback is welcome!)

I haven't ever written up a tutorial, but here's a technical report from someone else that gives an overview:

https://lucris.lub.lu.se/ws/portalfiles/portal/61129581/auto...

This is a timely book. Maybe more interesting (at least to me) than the recent results of AI research is the application of techniques used in the field applied elsewhere.

For my work going forward catering to automatic differentiation in the code is a no-brainer.

Why did the deep learning model cross the road? Because it was smooth and differentiable

TBH I would hope this is a Jax deep dive book

i don't know why people write these things. it's an absolute hodge-podge of theorem/proofs/results/techniques with no unifying theme other than "CALCULUS". so it's a pretty bad math book to actually learn math from (you can always spot a pedagogically unsound math book by its lack of exercises). the book doesn't even have any code in it which is surprising considering it has "programming" in the title.

actually i know why people write these but i still don't know why they publish them: this is a phase everyone goes through in their "math" life where they look back on everything they've learned hastily between undergrad/phd/postdoc (or whatever) and they have the urge to formalize/crystallize. everyone has the urge - i had a late-career QFT prof tell me that he was excited to take his sabbatical so that he could finally do all of the exercises in peskin&schroeder for real real and type it all up neatly.

i've done it too, in-the-small (some very nice notes that i'm proud of, on various things). you sit down, make your list of things, pull up all of the books/papers you're going to use as references and you start essentially transcribing - but you tell yourself you're putting your own spin on it (adding ample "motivation"). and it's all fine and healthy and gratifying for you and yourself alone. but i don't think i'd ever imagine to myself "well hmm my organization of these topics is going to be useful for other people i should put it out there for the world to see". but that's just me.

ninja edit:

before someone jumps down my throat about "what's the harm?". the harm is n00b/undergrads/young people/etc will look at this and think this is the right way to learn this material and some of them will even make an attempt to learn the material from this thing and they'll struggle and fail and be discouraged - i speak from experience! it's not a good thing for the community. sure maybe 1 in a 100 can learn this stuff from just reading a monograph (what these things used to be called...) but that's the exception that proves the rule.

It’s an introduction to a relatively niche new subfield. If I (an expert in the field but not the subfield) want to learn about differentiable programming, my only option before this monograph was to read through tens of random papers which use different presentation styles, terminology etc. Now I can read through the second half of this, around 100 pages, and jump back to the first half if there’s a prerequisite I don’t know.

That’s how most subfields are born. Assorted papers -> monograph -> textbook. The first arrow is defining the subfield as a discrete topic, which is immensely valuable. Only after you have that you can start optimizing for presentation to nonexperts.

The thing I like about this is that it frames all these optimization techniques + AD, etc. in the context of control flow and not just in the context of some trending neural network architecture. It doesn't assume you'll be using these techniques in a specific bubble, it gives the rest of us access to a broader perspective that experienced researchers have been slowly brewing for decades.

I've been trying to learn about applying gradient descent to a non-neural network problem, following a paper, and have found it very difficult to find introductory resources or code libraries that aren't explicitly geared toward training neural networks and running inference on them.

Differentiable programming is hardly a "subfield" it can be explained in a paragraph if you know calculus well. If there is any subfield, it's in researching specific compiler optimizations.

Well clearly not, since at least 100 pages of content here are specifically about differentiable programming and not prerequisites :)

More seriously, it's about doing the impossible. Formally, some functions are nondifferentiable, period. But it would be cool if we could actually "more or less" differentiate them. For that we'll necessarily need a bag of tricks which is now coalescing into "techniques" and "principles".

Cf. numerical analysis. It takes a page or two to set up your definitions and show that many functions are badly conditioned, period. And yet we still want to compute them, so we've been building the bag of tricks for almost a century now.

> not prerequisites

Almost the entire document is undergrad numerical topics (finite difference methods, maximum likelihood, Jacobins, hessians, newtons method, etc). This is all well covered material that is soundly not research.

hey look someone that actually knows what they're talking about!

You’re getting hit on a bunch of different things here, but I’d like to focus on two:

  it's an absolute hodge-podge of theorem/proofs/results/techniques with no unifying theme other than "CALCULUS".

I mean, yes…? Maybe I’m a terrible programmer but I’ve never applied calculus to my work in any real way. A book that’s just “calculus applications for software design” seems quite useful, and quite unrelated to teaching “math” in a direct way.

  and it's all fine and healthy and gratifying for you and yourself alone. but i don't think i'd ever imagine to myself "well hmm my organization of these topics is going to be useful for other people i should put it out there for the world to see". but that's just me.

Doesn’t this apply to all books of any kind? How do you know what if you can write a book before you try? I feel like “transcribe papers with a spin on them” is a perfect description of Russel and Norvig’s AI book, and many find it valuable. Is this just a math-specific criticism?

Either way, thanks for the very interested contrarian comment! You’re well spoken and I do love a discussion more than “wow this looks useful”, which is all I was prepared to give.

> I mean, yes…? Maybe I’m a terrible programmer but I’ve never applied calculus to my work in any real way. A book that’s just “calculus applications for software design” seems quite useful, and quite unrelated to teaching “math” in a direct way.

that's not what this book is. like i said in the first sentence right there in the part right before the all caps calculus - this is book is a compendium of theorem/proofs/etc. very little actual software.

> Doesn’t this apply to all books of any kind?

again, i already covered this: a pedadogically sound textbook will have exercises and structure/themes/etc rather than just 120 "propositions".

Taste-wise, I'd prefer these sorts of books to just briefly state facts and spend most of the time on building intuition, but just putting it all together in one place is nice in and of itself. I've had to re-derive all this shit on my own the last six months, and it would have been helpful to have a moderately complete compendium of the average practitioner's working knowledge available as a starting reference (admittedly, the first half is a hodge-podge that sort of backgrounds the second). It serves the same purpose as a good "survey" paper.

Taking ordinary calculus knowledge and expanding it to differentiable programming is hard. Even if-else branches have nuances which, at first glance, it looks like the authors didn't handle. They confidently state, for example, that the derivatives of predicate variables are uninformative, but the veracity of that statement depends on the local properties of the branches in question. It's often true for "mathy" conditional statements and often not true for more computer-programming-esque if-statements. Separating a particular representation of a computation from what it actually computes and obtaining accurate partial derivatives is _hard_. Jax and Julia both have bugs on that front, and it's good that people are trying to make this knowledge more accessible (admittedly, not very well on that front IMO, but the other sections I spot-checked all look to be higher quality).