Backpropagation is a leaky abstraction (2016)

https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b

by swatson741 • 9 months ago

353 160 9 months ago

karpathy.medium.com

joshdavham4• 9 months ago

Given that we're now in the year 2025 and AI has become ubiquitous, I'd be curious to estimate what percentage of developers now actually understand backprop.

It's a bit snarky of me, but whenever I see some web developer or product person with a strong opinion about AI and its future, I like to ask "but can you at least tell me how gradient descent works?"

I'd like to see a future where more developers have a basic understanding of ML even if they never go on to do much of it. I think we would all benefit from being a bit more ML-literate.

kojoru• 9 months ago

I'm wondering: how can understanding gradient descent help in building AI systems on top of LLMs? To mee it feels like the skills of building "AI" are almost orthogonal to skills of building on top of "AI"

joshdavham4• 9 months ago

I take your point in that they are mostly orthogonal in practice, but with that being said, I think understanding how these AI's were created is still helpful.

For example, I believe that if we were to ask the average developer about why LLM's behave randomly, they would not be able to answer. This to me exposes a fundamental hole in their knowledge of AI. Obviously one shouldn't feel bad about not knowing the answer, but I think we'd benefit from understanding the basic mathematical and statistical underpinnings on these things.

HarHarVeryFunny11• 9 months ago

Sure, but it'd be similar to being a software developer and not understanding roughly what a compiler does. In a world full of neural network based technology, it'd be a bit lame for a technologist not to at least have a rudimentary understanding of how it works.

Nowadays, fine tuning LLMs is becoming quite mainstream, so even if you are not training neural nets of any kind from scratch, if you don't understand how gradients are used in the training (& fine tuning) process, then that is going to limit your ability to fully work with the technology.

lock1• 9 months ago

  > I'd like to see a future where more developers have a basic understanding of ML even if they never go on to do much of it. I think we would all benefit from being a bit more ML-literate.

Why "ML-literate" specifically? Also, there are some people against calculus and statistic in CS curriculum because it's not "useful" or "practical", why does ML get special treatment here?

Plus, I don't think a "gotcha" question like "what is gradient descent" will give you a good signal about someone if it get popularized. It probably will lead to the present-day OOP cargo cult, where everyone just memorizes whatever their lecturer/bootcamp/etc and repeats it to you without actually understanding what it does, why it's the preferred method over other strategies, etc.

joshdavham4• 9 months ago

Why "ML-literate" specifically?

We could also say AI-literate too, I suppose. I guess I just like to focus on ML generally because 1) most modern AI is possible only due to ML and 2) it’s more narrow and emphasizes the low level of how AI works.

confirmmesenpai3• 9 months ago

so if you want to have a strong opinion on electric cars you need to be able to explain how an electric engine works right?

oceanplexian• 9 months ago

I’d say so, the hallmark of being a car guy is understanding the basics, like the difference between a four cylinder and a six cylinder, a turbocharger from a supercharger, the different types of gearboxes (DCT vs AT or a CVT), and so on. They all affect the feel, capabilities, and limitations of the car.

Electric cars have similar complexities and limitations, for example the Bolt I owned could only go ~92MPH due to limitations in the gearing as a result of having a 1 speed gearbox. I would expect someone with a strong opinion of a car to know something as simple as the top speed.

chermi• 9 months ago

Depends on what exactly the opinion is, but generally I'd say yes. If it's about their looks, maybe not.. but even then understanding the basics that determine things like not needing air intake, exhaust, the placement of batteries, etc. can be helpful.

If it's about the supply chain, understanding at least the requirements for magnets is helpful.

On way to make sure you understand all of these things is to understand the electric motor. But you could learn the separate pieces of knowledge on the fly too.

The more you understand the fundamentals of what you're talking about, the more likely you are to have genuine insight because you can connect more aspects of the problem and understand more of the "why".

TL;DR it depends, but it almost always helps.

esafak3• 9 months ago

The electric engine? I won't give someone the time of day if they don't understand the battery chemistry.

augment_me• 9 months ago

Impossible requirement. The inherent quality of abstractions is to allow us to get more done without understanding everything. We dont write raw assembly for the same reason, you dont make fire by rubbing sticks, you dont go hunting for food in the woods, etc.

There is no need for the knowledge that you propose in a world where this is solved, you will achieve more goals by utilizing higher-level tools.

joshdavham4• 9 months ago

I get your point and this certainly applies to most modern computing where each new layer of abstraction becomes so solid and reliable that devs can usually afford to just build on top of it without worrying about how it works. I don’t believe this applies to modern AI/ML however. Knowing the chain rule, gradient descent and basic statistics IMO is not the same level of solid as other abstractions in computing. We can’t afford to not know these things. (At least not yet!)

esafak3• 9 months ago

As ML becomes 'democratized' the fraction of people with technical understanding will trend towards zero. They will operate at a higher level of abstraction, and worry about novel things like hallucinations, prompt injection, and misinformation. Such is the nature of advancement.

gchadwick2• 9 months ago

Karpathy's contribution to teaching around deep learning is just immense. He's got a mountain of fantastic material from short articles like this, longer writing like https://karpathy.github.io/2015/05/21/rnn-effectiveness/ (on recurrent neural networks) and all of the stuff on YouTube.

Plus his GitHub. The recently released nanochat https://github.com/karpathy/nanochat is fantastic. Having minimal, understandable and complete examples like that is invaluable for anyone who really wants to understand this stuff.

throwaway290• 9 months ago

And to all the LLM heads here, this is his work process:

Yesterday I was browsing for a Deep Q Learning implementation in TensorFlow (to see how others deal with computing the numpy equivalent of Q[:, a], where a is an integer vector — turns out this trivial operation is not supported in TF). Anyway, I searched “dqn tensorflow”, clicked the first link, and found the core code. Here is an excerpt:

Notice how it's "browse" and "search" not just "I asked chatgpt". Notice how it made him notice a bug

stingraycharles• 9 months ago

First of all, this is not a competition between “are LLMs better than search”.

Secondly, the article is from 2016, ChatGPT didn’t exist back then

confirmmesenpai3• 9 months ago

what you did here is called confirmation bias.

I think congrats again to OpenAI for cooking with GPT-5 Pro. This is the third time I've struggled on something complex/gnarly for an hour on and off with CC, then 5 Pro goes off for 10 minutes and comes back with code that works out of the box. I had CC read the 5 Pro version and it wrote up 2 paragraphs admiring it (very wholesome). If you're not giving it your hardest problems you're probably missing out.

https://x.com/karpathy/status/1964020416139448359

kubb3• 9 months ago

I was slightly surprised that my colleagues, who are extremely invested in capabilities of LLMs, didn’t show any interest in Karpathy’s communication on the subject when I recommended it to them.

Later I understood that they don’t need to understand LLMs, and they don’t care how they work. Rather they need to believe and buy into them.

They’re more interested in science fiction discussions — how would we organize a society where all work is done by intelligent machines — than what kinds of tasks are LLMs good at today and why.

teiferer3• 9 months ago

Which is terrible. That's the root of all the BS around LLMs. People lacking understanding of what they are and ascribing capabilities which LLMs just don't have, by design. Even HN discussions are full of that. Even though this page literally has "hacker" in its name.

Al-Khwarizmi5• 9 months ago

What's wrong or odd about that? You can like a technology as a user and not want to delve into how it works (sentence written by a human despite use of "delve"). Everyone should have some notions on what LLMs can or cannot do, in order to use them successfully and not be misguided by their limitations, but we don't need everyone to understand what backpropagation is, just as most of us use cars without knowing much about how an internal combustion engine works.

And the issue you mention in the last paragraph is very relevant, since the scenario is plausible, so it is something we definitely should be discussing.

arisAlexis• 9 months ago

Obviously they are more focused on making something that works

CuriouslyC• 9 months ago

I think there are a lot of people who just don't care about stuff like nanochat because it's exclusively pedagogical, and a lot of people want to learn by building something cool, not taking a ride on a kiddie bike with training wheels.

android521• 9 months ago

Do you go deep into molecular biology to see how it works , it is much more interesting and important

danielbln2• 9 months ago

I'm personally very interested in how LLMs work under the hood, but I don't think everyone who uses them as tools needs that. I don't know the wiring inside my drill, but I know how to put a hole in my wall and not my hand regardless.

amelius• 9 months ago

But the question is if you have a better understanding of LLMs from a user's perspective, or they.

tanepiper• 9 months ago

If everyone had to understand how carburettors, engines and break systems work; to be able to drive a car - rather than just learn to drive and get from A to B - I'm guessing there would be a lot less cars on the road.

(Thinking about it, would that necessarily be a bad thing...)

miki123211• 9 months ago

Not everybody who drives a car (even as a professional driver) knows how to make one.

If you live in a world of horse carriages, you can be thinking about what the world of cars is going to be like, even if you don't fully understand what fuel mix is the most efficient or what material one should use for a piston in a four-stroke.

drivebyhooting• 9 months ago

I have a naive question about backprop and optimizers.

I understand how SGD is just taking a step proportional to the gradient and how backprop computes the partial derivative of the loss function with respect to each model weight.

But with more advanced optimizers the gradient is not really used directly. It gets per weight normalization, fudged with momentum, clipped, etc.

So really, how important is computing the exact gradient using calculus, vs just knowing the general direction to step? Would that be cheaper to calculate than full derivatives?

mgh95• 9 months ago

But with more advanced optimizers the gradient is not really used directly. It gets per weight normalization, fudged with momentum, clipped, etc.

Why would these things be "fudging"? Vanishing gradients (see the initial batch norm paper) are a real thing, and ensuring that the relative magnitudes are in some sense "smooth" between layers allows for an easier optimization problem.

So really, how important is computing the exact gradient using calculus, vs just knowing the general direction to step? Would that be cheaper to calculate than full derivatives?

Very. In high dimensional space, small steps can move you extremely far from a proper solution. See adversarial examples.

ssivark• 9 months ago

So really, how important is computing the exact gradient using calculus, vs just knowing the general direction to step? Would that be cheaper to calculate than full derivatives?

Yes, absolutely -- a lot of ideas inspired by this have been explored in the field of optimization, and also in machine learning. The very idea of "stochastic" gradient descent using mini-batches basically a cheap (hardware compatible) approximation to the gradient for each step.

For a relatively extreme example of how we might circumvent the computational effort of backprop, see Direct Feedback Alignment: https://towardsdatascience.com/feedback-alignment-methods-7e...

Ben Recht has an interesting survey of how various learning algorithms used in reinforcement learning relate with techniques in optimization (and how they each play with the gradient in different ways): https://people.eecs.berkeley.edu/~brecht/l2c-icml2018/ (there's nothing special about RL... as far as optimization is concerned, the concepts work the same even when all the data is given up front rather than generated on-the-fly based on interactions with the environment)

danielmarkbruce• 9 months ago

Calculus isn't that complicated, at least not what's done in backprop.

How do you propose calculating the "general direction" ?

And, an example "advanced optimizer" - AdamW - absolutely uses gradients. It just does more, but not less.

blackbear_• 9 months ago

Two thoughts:

how important is computing the exact gradient using calculus

Normally the gradient is computed with a small "minibatch" of examples, meaning that on average over many steps the true gradient is followed, but each step individually never moves exacty along the true gradient. This noisy walk is actually quite beneficial for the final performance of the network https://arxiv.org/abs/2006.15081 , https://arxiv.org/abs/1609.04836 so much so that people started wondering what is the best way to "corrupt" this approximate gradient even more to improve performance https://arxiv.org/abs/2202.02831 (and many other works relating to SGD noise)

vs just knowing the general direction to step

I can't find relevant papers now, but I seem to recall that the Hessian eigenvalues of the loss function decay rather quickly, which means that taking a step in most directions will not change the loss very much. That is to say, you have to know which direction to go quite precisely for an SGD-like method to work. People have been trying to visualize the loss and trajectory taken during optimization https://arxiv.org/pdf/1712.09913 , https://losslandscape.com/

raindeer2• 9 months ago

The first bit is why it is called Stochastic gradient decent. You follow the gradient of a randomly chosen minibatch at each step. It basically makes you "vibrate" down along the gradient.

imtringued2• 9 months ago

All first order methods use the gradient or Jacobian of a function. Calculating the first order derivatives is really cheap.

Non-stochastic gradient descent has to optimize over the full dataset. This doesn't matter for non-machine learning applications, because often there is no such thing as a dataset in the first place and the objective has a small fixed size. The gradient here is exact.

With stochastic gradient descent you're turning gradient descent into an online algorithm, where you process a finite subset of the dataset at a time. Obviously the gradient is no longer exact, you still have to calculate it though.

Seems like "exactness" is not that useful of a property for optimization. Also, I can't stress it enough, but calculating first order derivatives is so cheap there is no need to bother. It's roughly 2x the cost of evaluating the function in the first place.

It's second order derivatives that you want to approximate using first order derivatives. That's how BFGS and Gauss-Newton work.

macleginn3• 9 months ago

It is possible to compute the approximate gradient (direction to step) without using the formulas: we can change the value of each parameter individually, compute the loss, set the values of all parameters in such a way that the loss is minimized, and then repeat. This means, however, that we have to do number-of-parameters forward passes for one optimization step, which is very expensive. With formulas, we can compute all these values in one backward pass.

GistNoesis• 9 months ago

computing the exact gradient using calculus

First of all, gradient computation with back-prop (aka reverse-mode automatic differentiation) is exact to numerical precision (except for edge-cases that are not relevant here) so it's not about the way of computing the gradient.

What Andrej is trying to tell is that when you create a model, you have freedom of design in the shape of the loss function. And that in this design what matters for learning is not so much the value of the loss function, but its slopes, and curvature (peaks and valleys).

The problematic case being flat valleys, surrounded by straight cliffs, (picture the grand canyon).

Advanced optimizers in deep learning like "Adam", are still first-order, with diagonal approximation of the curvature, which mean the optimizer in addition to the gradient it has an estimate of the scale sensitivity of each parameter independently. So the cheap thing it can reasonably do is modulate the gradient with this scale.

The length of the gradient vector, being often problematic, what optimizers would usually do was something called "line-search", which is determine the optimal step-size along this direction. But the cost of doing that is usually between 10-100 evaluation of the cost function which is often not worth the effort in the noisy stochastic context, compared to just taking a smaller step multiple times.

Higher-order optimizers necessitate that the loss function is twice differentiable, so non-linearities like relu, which are cheap to calculate can't be used.

Lower-order global optimizers don't even necessitate the gradient, which is useful when the energy-function landscape has lots of local minima, (picture an egg-box).

HarHarVeryFunny11• 9 months ago

You don't need exact gradients, since gradient descent is self-correcting (which can make it hard to find gradient calculation bugs!). One approach using inexact gradients is to use predicted "synthetic gradients" which avoids needing to wait for backward pass for weight updates.

ainch• 9 months ago

That's an interesting idea, it sounds similar to the principles behind low precision models like BitNet (where each weight is +-1 or 0).

That said, I know Deepseek use fp32 for their gradient updates even though they use fp8 for inference. And a recent paper shows that RL+LLM training is shakier at bf16 than fp16, which would both imply that numerical precision in gradients still matters.

joe_the_user2• 9 months ago

There are a lot of approximation methods involved in training neural networks. But the main thing that while learning calculus is a challenging, actually calculating the derivative of a function at a point using algorithmic differentiation is actually extremely fast and exact, nearly as exact as calculating the function's value itself and inherently more efficient than finite difference approximations to the derivative. Algorithmic differentiation is nearly "magic".

But remember, that is for taking the derivative at a single data point - what's hard is the average derivative over the entire set of points and that's where sampling and approximations (SGD etc)comes in.

killerstorm• 9 months ago

I don't think there's a way to find "general direction" without actually using calculus.

If you write down an explicit expression for partial derivatives, it will contain sums. The sign (which kind of defines the general direction) is affected by what's in the sum, and you can't avoid calculating it.

emil-lp• 9 months ago

... (2016)

9 years ago, 365 points, 101 comments

https://news.ycombinator.com/item?id=13215590

alyxya• 9 months ago

More generally, it's often worth learning and understanding things one step deeper. Having a more fundamental understanding of things explains more of the "why" behind why some things are the way they are, or why we do some things a certain way. There's probably a cutoff point for balancing how much you actually need to know though. You could potentially take things a step further by writing the backwards pass without using matrix multiplication, or spend some time understanding what the numerical value of a gradient means.

phplovesong• 9 months ago

Sidenote why are people still using medium?

evbogue• 9 months ago

article is from 2016

joaquincabezas5• 9 months ago

I took a course in my Master's (URV.cat) where we had to do exactly this, implementing backpropagation (fwd and backward passes) from a paper explaining it, using just basic math operations in a language of our choice.

I told everyone this was the best single exercise of the whole year for me. It aligns with the kind of activity that I benefit immensely but won't do by myself, so this push was just perfect.

If you are teaching, please consider this kind of assignments.

P.S. Just checked now and it's still in the syllabus :)

LPisGood• 9 months ago

I did this in highschool from some online textbook in plain Java. I recall implementing matrix multiplication myself being the hardest part.

I made a UI that showed how the weights and biases changed throughout the training iterations.

aDyslecticCrow2• 9 months ago

I had a whole course just about how computers do maths. Matrix multiplication, linear fit, finding eigenvectors, multiplication and division, square root, solving linear systems, numerically calculating differential equations, spline interpolation, FEM analysis.

"Computers are good at maths" is normally a pretty obvious statement... but many things we take for granted from analytical mathematics, is quite difficult to actually implement in a computer. So there is a mountain of clever algorithms hiding behind some of the seemingly most obvious library operations.

One of the best courses I've ever had.

blitzar• 9 months ago

The difference in understanding (for me and how my brain works) between reading the paper in what appears to be a future or past alien language & doing a minimal paper / code example is massive.

joaquincabezas5• 9 months ago

same here, even more if I'm doing it over few days and different angles

mkl• 9 months ago

Is that paper publicly available?

joaquincabezas5• 9 months ago

Hi! It's not public, it's part of the https://www.urv.cat/en/studies/master/courses/computer-secur... and I've not found it online

joaquincabezas5• 9 months ago

just found it! but it's private, I can send it to you if interested but not to publish it

littlestymaar2• 9 months ago

I was happy to see Karpathy writing a new blog post instead of simply Twitter threads, but when I opened the link I just got dispointed to realize it's from 9 years ago…

I really hate what Twitter did to blogging…

Geee• 9 months ago

He has a new blog at https://karpathy.bearblog.dev/blog/

littlestymaar2• 9 months ago

Oh, I wasn't aware of it, thank you very much!

jamesblonde• 9 months ago

I have to be contrarian here. The students were right. You didn't need to learn to implement backprop in NumPy. Any leakiness in BackProp is addressed by researchers who introduce new optimizers. As a developer, you just pick the best one and find good hparams for it.

_diyar• 9 months ago

From the perspective of the university, the students are being trained to become researchers, not engineers.

PeterStuer• 9 months ago

The problem with your reasoning is you never tackle your "unknown unknowns". You just assume they are "known unknowns".

Diving through the abstraction reveals some of those.

gchadwick2• 9 months ago

It's for a CS course at Stanford not a PyTorch boot camp. It seems reasonable to expect some level of academic rigour and need to learn and demonstrate understanding of the fundamentals. If researchers aren't learning the fundamentals in courses like these where are they learning them?

You've also missed the point of the article, if you're building novel model architectures you can't magic away the leakiness. You need to understand the back prop behaviours of the building blocks you use to achieve a good training run. Ignore these and what could be a good model architecture with some tweaks will either entirely fail to train or produce disappointing results.

Perhaps you're working at a level of bolting pre built models together or training existing architectures on new datasets but this course operates below that level to teach you how things actually work.

froobius• 9 months ago

Any leakiness in BackProp is addressed by researchers who introduce new optimizers
As a developer, you just pick the best one and find good hparams for it

It would be more correct to say: "As a developer, (not researcher), whose main goal is to get a good model working — just pick a proven architecture, hyperparameters, and training loop for it."

Because just picking the best optimizer isn't enough. Some of the issues in the article come from the model design, e.g. sigmoids, relu, RNNs. And some of the issues need to be addressed in the training loop, e.g. gradient clipping isn't enabled by default in most DL frameworks.

And it should be noted that the article is addressing people on the academic / research side, who would benefit from a deeper understanding.

HarHarVeryFunny11• 9 months ago

The problem isn't with backprop itself or the optimizer - it's potentially in (the dervatives of) the functions you are building the neural net out of, such as the Sigmoid and ReLU examples that Karpathy gave.

Just because the framework you are using provides things like ReLU doesn't mean you can assume someone else has done all the work and you can just use these and expect them to work all the time. When things go wrong training a neural net you need to know where to look, and what to look for - things like exploding and vanishing gradients.

vrighter5• 9 months ago

And where do the researchers supposed to come from, exactly?

brcmthrowaway• 9 months ago

Do LLMs still use backprop?

ForceBru• 9 months ago

Are LLMs still trained by (variants of) stochastic GRADIENT descent? AFAIK what used to be called "backprop" is nowadays known as "automatic differentiation". It's widely used in PyTorch, JAX etc

imtringued2• 9 months ago

Gradient descent doesn't matter here. Second order and higher methods still use lower order derivatives.

Back propagation is reverse mode auto differentiation. They are the same thing.

And for those who don't understand what back propagation is, it is just an efficient method to calculate the gradient for all parameters.

samsartor• 9 months ago

Yes. Pretraining and fine-tuning use standard Adam optimizers (usually with weight-decay). Reinforcement learning has been the odd-man out historically, but these days almost all RL algorithms also use backprop and gradient descent.

stared• 9 months ago

The original title is "Yes you should understand backprop" - which is good and descriptive.

dpflan• 9 months ago

Agree, better title for this post; the fact that back prop is a leaky abstraction is a reason one should understand it and know how to do the mechanics by hand to truly experience it and develop understanding and intuition. Software / code abstracting away even more of the process leaves it open to magical thinking. I had to do hand calculations and convolutions in my Deep Learning graduate school course.

xpe4• 9 months ago

Yep. Also, I don’t find the metaphorical connection to leaky abstractions useful at all. It feels strained.

WithinReason4• 9 months ago

Karpathy suggests the following error:

  def clipped_error(x): 
    return tf.select(tf.abs(x) < 1.0, 
                   0.5 * tf.square(x), 
                   tf.abs(x) - 0.5) # condition, true, false

Following the same principles that he outlines in this post, the "- 0.5" part is unnecessary since the gradient of 0.5 is 0, therefore -0.5 doesn't change the backpropagated gradient. In addition, a nicer formula that achieves the same goal as the above is √(x²+1)

macleginn3• 9 months ago

If we don't subtract from the second branch, there will be a discontinuity around x = 1, so the derivative will not be well-defined. Also the value of the loss will jump at this value, which will make it hard to inspect the errors, for one thing.

WithinReason4• 9 months ago

No, that's not how backprop works. There will be no discontinuity in a backpropagated gradient.

kingstnap• 9 months ago

You do that to make things smoother when plotted. You could in theory add some crazy stairstep that adds a hundred to the middle part. It would make your loss curves spike and increase towards convergence but then those spikes are just visual artifacts from doing weird discontinuous nonsense with yoru loss.

slashdave• 9 months ago

square roots are expensive

WithinReason4• 9 months ago

they are negligible, especially when the post was written when ops were not fused. The extra memory you need to store the extra tensors when you use the original version is more expensive

joaquincabezas5• 9 months ago

off-topic, anybody knows what's going on with EurekaLabs? It's been a while since the announcement

meken• 9 months ago

He gives an update in the Dwarkesh interview:

https://youtu.be/lXUZvyajciY?si=vbqKDOOY7l-491Ka&t=7028

Not too many details on timeline - just that he's working on it.

leobg2• 9 months ago

He does have a history of abandoning projects. OpenAI. Tesla. OpenAI again...

Then again, it might have been the corporate stuff that burned him out rather than the engineering.

leobg2• 9 months ago

Also, he published nanochat 3 weeks ago [0], and he says in the readme:

nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.

[0] https://github.com/karpathy/nanochat

away74etcie2• 9 months ago

Karpathy's work on large datasets for deep neural flow is conceiving of the "backward pass" as the preparation for initializing the mechanics for weight ranges, either as derivatives in -10/+10 statistic deviations.

sebastianconcpt2• 9 months ago

This comment:

“Why do we have to write the backward pass when frameworks in the real world, such as TensorFlow, compute them for you automatically?”

worries me because is structured with the same reasoning of "why we have to demonstrate we understand addition if in the real world we have calculators"

rossdavidh• 9 months ago

The counter-argument would be that you can make excellent arguments for why we should understand what the compiler is doing, understand what the transistors are doing (e.g. to understand the limitations and risks of overclocking), understand how sort algorithms work (doing sort algorithms manually was at one time a typical interview question). It's not that these things aren't useful, but whether or not that learning would be useful is not the right question.

Is this _more_ useful than the other things, which I could be learning but won't because I spent the time and effort to learn this instead?

We have a finite capacity for learning, if for no other reason then at least because we have a finite amount of time in this life, and infinite topics to learn (and there are plenty of other constraints besides time). The reason given for learning this topic, is that it has hidden failure modes which you will not be on the lookout for if you didn't know how it worked "under the hood".

Is this a good enough reason to spend time learning this rather than, say, how to model the physics of the system you're training the neural network to deal with? Tough question; maybe, maybe not. If you have time to learn both, do that, but if not, then you will have to choose which is most important. And in our education system, we do things like teach calculus but not intermediate statistics, and it would have been better to do the opposite for something like 90% of the people taking calculus.

That said, I've implemented backpropagation multiple times, it's a good way to evaluate a new language (just complex enough to reveal problems, not so complex that it takes forever).

sebastianconcpt2• 9 months ago

But you went all the way to the extreme. There is a gradient of know-how that is essential for mastery.

You can ignore every detail of a transistor, but knowing it can be like a valve is enough to understand that it can model consistently a logic gate of a stream of electrons and that can be used to compute.

If wouldn't be for that, then the laws that govern your whole computer become magic for you brain.

I fear the consequences of minds that value more their laziness rather to understand, let's say with 5 degrees deep of "why", the things in reality work the way they do. The alternative is that they are hallucinating all the time on narratives that can be psychotic or real and they are not equipped to discern which is which.

vrighter5• 9 months ago

You don't have to, but of that list at the beginning of your comment, I actually did study them a bit on my own just for shits and giggles. And I am grateful for that because even if I don't use them directly, they have directly influenced my thinking when working at a higher level of abstraction.

For example: The recently posted-about pass-by-value overhead. And it seems that some AMD processors have a 4k aliasing issue. Knowing a tiny bit about how CPU caches actually work internally immediately made me go "oh.... I can see how that issue could arise due to how associative caches are typically implemented".

You don't necessarily use that stuff directly, but you will use them a lot more than you might think, even subconsciously.

nirinor3• 9 months ago

Its a nit pick, but backpropagation is getting a bad rep here. These examples are about gradients+gradient descent variants being a leaky abstraction for optimization [1].

Backpropagation is a specific algorithm for computing gradients of composite functions, but even the failures that do come from composition (multiple sequential sigmoids cause exponential gradient decay) are not backpropagation specific: that's just how the gradients behave for that function, whatever algorithm you use. The remedy, of having people calculate their own backwards pass, is useful because people are _calculating their own derivatives_ for the functions, and get a chance to notice the exponents creeping in. Ask me how I know ;)

[1] Gradients being zero would not be a problem with a global optimization algorithm (which we don't use because they are impractical in high dimensions). Gradients getting very small might be dealt with by with tools like line search (if they are small in all directions) or approximate newton methods (if small in some directions but not others). Not saying those are better solutions in this context, just that optimization(+modeling) are the actually hard parts, not the way gradients are calculated.

xpe4• 9 months ago

Yes. No need to be apologetic or timid about it — it’s not a nit to push back against a flawed conceptual framing.

I respect Karpathy’s contributions to the field, but often I find his writing and speaking to be more than imprecise — it is sloppy in the sense that it overreaches and butchers key distinctions. This may sound harsh, but at his level, one is held to a higher standard.

embedding-shape• 9 months ago

often I find his writing and speaking to be more than imprecise

I think that's more because he's trying to write to an audience who isn't hardcore deep into ML already, so he simplifies a lot, sometimes to the detriment of accuracy.

At this point I see him more as a "ML educator" than "ML practitioner" or "ML researcher", and as far as I know, he's moving in that direction on purpose, and I have no qualms with it overall, he seems good at educating.

But I think shifting the mindset of what the purpose of his writings are maybe help understand why sometimes it feels imprecise.

HarHarVeryFunny11• 9 months ago

Whoever chose this topic title perhaps did him a disservice in suggesting he said the problem was backprop itself, since in his blog post he immediately clarifies what he meant by it. It's a nice pithy way of stating the issue though.

mitthrowaway2• 9 months ago

But Karpathy is completely right; students who understand and internalize how backprop works, having implemented it rather than treating it as a magic spell cast by TF/PyTorch, will also be able to intuitively understand these problems of vanishing gradients and so on.

Sure, instead of "the problem with backpropagation is that it's a leaky abstraction" he could have written "the problem with not learning how back propagation works and just learning how to call a framework is that backpropagation is a leaky abstraction". But that would be a terrible sub-heading for an introductory-level article for an undergraduate audience, and also unnecessary because he already said that in the introduction.

fjdjshsh• 9 months ago

I get your point, but I don't think your nit-pick is useful in this case.

The point is that you can't abstract away the details of back propagation (which involve computing gradients) under some circumstances. For example, when we are using gradient descend. Maybe in other circumstances (global optimization algorithm) it wouldn't be an issue, but the leaky abstraction idea isn't that the abstraction is always an issue.

(Right now, back propagation is virtually the only way to calculate gradients in deep learning)

nirinor3• 9 months ago

So, are computing gradients details of backpropagation that it is failing to abstract over, or are gradients the goal that backpropagation achieves? It isn't both, its just the latter.

This is like complaining about long division not behaving nicely when dividing by 0. The algorithm isn't the problem, and blaming the wrong part does not help understanding.

It distracts from what is actually helping which is using different functions with nicer behaving gradients, e.g., the Huber loss instead of quadratic.

xpe4• 9 months ago

Karpathy is butchering the metaphor. There is no abstraction here. Backprop is an algorithm. Automatic differentiation is a technique. Neither promises to hide anything.

I agree that understanding them is useful, but they are not abstractions much less leaky abstractions.

t-vi• 9 months ago

It seems to me that in 2016 people did (have to) play a lot more tricks with the backpropagation than today. Back then it was common to meddle with gradients in between the gradient propagation.

For example, Alex Graves's (great! with attention) 2013 paper "Sequence Generation with Recurrent Neural Networks" has this line:

One difficulty when training LSTM with the full gradient is that the derivatives sometimes become excessively large, leading to numerical problems. To prevent this, all the experiments in this paper clipped the derivative of the loss with respect to the network inputs to the LSTM layers (before the sigmoid and tanh functions are applied) to lie within a predefined range.

with this footnote:

In fact this technique was used in all my previous papers on LSTM, and in my publicly available LSTM code, but I forgot to mention it anywhere—mea culpa.

That said, backpropagation seems important enough to me that I once did a specialized videocourse just about PyTorch (1.x) autograd.

HarHarVeryFunny11• 9 months ago

It seems to me that in 2016 people did (have to) play a lot more tricks with the backpropagation than today

Perhaps, but maybe because there was more experimentation with different neural net architectures and nodes/layers back then?

Nowadays the training problems are better understood, clipping is supported by the frameworks, and it's easy to find training examples online with clipping enabled.

The problem itself didn't actually go away. ReLU (or GELU) is still the default activation for most networks, and training an LLM is apparently something of a black art. Hugging Face just released their "Smol Training Playbook: a distillation of hard earned knowledge to share exactly what it takes to train SOTA LLMs", so evidentially even in 2025 training isn't exactly a turn-key affair.

joe_the_user2• 9 months ago

I wonder if its just about the important neural nets now being trained by large, secretive corporations that aren't interested in sharing their knowledge.

raindear• 9 months ago

Are dead ReLUs still a pronlem today? Why not?

intelkishan• 8 months ago

There are alternative activation functions, which are also widely used.

mirawelner• 9 months ago

I feel like my learning curve for AI is:

1) Learn backprop, etc, basic math

2) Learn more advanced things, CNNs, LMM, NMF, PCA, etc

3) Publish a paper or poster

4) Forget basics

5) Relearn that backprop is a thing

repeat.

Some day I need to get my education together.

Huxley1• 9 months ago

When I first started learning deep learning, I only had a vague idea of how backprop worked. It wasn't until I forced myself to implement it from scratch that I realized it was not magic after all. The process was painful, but it gave me much more confidence when debugging models or trying to figure out where the loss was getting stuck. I would really recommend everyone in deep learning try writing it out by hand at least once.

vrighter5• 9 months ago

Implementing one also finally gave me a way to intuitively grasp (and remember) the chain-rule from calculus.

rao-v• 9 months ago

I wonder if in the long term, with compute being cheap and parameter volumes being the constraint, if it will make sense to train models to be robust to different activation functions that look like ReLU (I.e. swish, gelu etc.)

You might even be able to do a ugly version of this (akin to dropout) where you swap activation functions (with adjusted scaling factors so they mostly yield similar output shapes to ReLU for most input) randomly during training. The point is we mostly know what an ReLU like activation function is supposed to do, so why should we care about the edge cases of the analytical limits of any specific one.

The advantage would be that you’d probably get useful gradients out of one of them(for training), and could swap to the computationally cheapest one during inferencing.

Backpropagation is a leaky abstraction (2016)

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