I've got a question. I'm having a bit of trouble phrasing it, so please don't assume I'm against learning for learning's sake, that's not the point.
Whenever I've tried to learn about LLMs, machine learning, etc. I always come out with a bad feeling, compared to learning about software.
Theoretical courses make me feel like it's all baby steps, unless you assume a level of commitment equivalent to a math degree. Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Now this feeling of "impracticality" is perfectly fine. It would be the expected outcome if I was taking a course on classic literature or modern art. But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
Did anyone have the same experience? any tips if so?
I think this is an outcome of playing with systems that we don’t actually understand very well on a deep level. We have found recipes that work, and have used graduate descent (so many PhD theses) to understand and optimize these systems, but we’ve built a model of language that also encodes reasoning, and those things are extraordinarily complex. Providing proper mathematical rigor for their explanation requires, as you say, the level of commitment equivalent to a math degree. Also, because language is so complicated and subtle, you need highly precise statistical models of it, and that just takes a shitload of data; hence the scale. You can train toy models at home, but they will suck because you actually need massive amounts of data to capture the nuance that language requires. And that’s to say nothing of multimodal models.
I think the level of interaction on forums like HN is a great example of the still-yawning gulf between LLMs and humans when it comes to encoding reasoning into language. Even with the self-selection that comes with posting here, think of how many times it is almost impossible to tell if posters are shit-posting, serious, angry, sock puppets, or just weird.
You say "...because you actually need massive amounts of data to capture the nuance that language requires..." but humans don't require that! So why do LLMs?
How many books did you read in your education? Far fewer that what LLMs read, no doubt. Of course we "have a life" and so we interact with hundreds of people, which is also a form of learning.
But in any case there's are fundamental differences here between these two systems (LLMs and humans) that we have not yet discerned clearly.
Far fewer than an LLM but then I “know” far less than an LLM. Sam Altman talked about a PhD level expert on any topic; if we expect a SOTA model to answer any question on any topic in a library, presumably that requires a library worth of books go into it?
Say I ask it who are the most beloved Indian cricketers and why, it might not be correct but it will have the right vocabulary - cricket terms and gameplay I’ve not heard of, player names and styles I don’t recognise, things critics and commentators like and dislike about game strategies. And it is like that for almost any topic.
If they hallucinate a Python function, it will look like a plausible function e.g. “hn.create_user_account()”. They don’t hallucinate like
# create HN user account
# requires eye of newt and frog leg
“Oh great spirit of the internet I request and command you make a new account…”
It’s a terrific amount of facts to be able to write even plausibly about tons of topics.
We don't need LLMs that can "answer any question on any topic in a library"!
We don't even need machines that can answer any question in a specific topic area. But we could use machines that can answer "I don't know." when they cannot compute the answer.
Consider the possibility of much-smaller LLMs that answer questions only in limited domains (physics, chemistry, math, medicine, etc.)?
Could not such LLMs exist? If not, why not? Would they not likely be more useful and far, far easier and cheaper to create and run (possibly even with far less hardware) than the current "know-all" LLMs? Also might they possibly avoid certain problems ( e.g., hallucinations, deceit, apparently undesirable behavior, etc.) associated with the "know-all" LLMs we now have?
I feel the intuition leads us astray here because the combined text of every science book ever written is much less than the size of a complete description of the human brain. To the hamburger of reason the library is a sesame seed.
We need to up the ante, we all know it's the Mumbai Indians. ;)
One can train a decent model at home, for a specific task but you are absolutely correct that in order to have a model that's an expert in any topic it requires training and data on all topics.
I nearly spit out coffee reading this. Amazing use of vocabulary; especially "... oh great spirit of the internet".
I sometimes jokingly explain to certain people not immersed in tech that sometimes saying the "magic words" (commands) from the "codex" (script) in the right sequence with a lot of hand waving allow "magic" (applications/web apps/etc.) to happen.
It generally brightens a patients day and makes things relatable to those non-technical, children, the elderly, or the like.
We have DNA though. It was subject to "pretraining" for billions of years. Alongside some epigenetic stuff
Maybe in the future we will have fewer robots interacting in the environment with continuous learning, to mimic the human experience, rather than a massive fleet of stateless, inference-only models
Humans don’t need that? How many words have you taken in from all possible sources by the time you’re 18? And many of those words came with additional data due to tone of voice, inflection and context. And then some of those (in-person, filmed art) came with incredibly subtle data from body language and facial expressions. I think we take in a massive corpus of data every year. Think just of any book you re-read: why did you do it? Did you glean additional insight?
We experience tons of data, but it's still much less than the pretraining step of LLMs
I think it's clear that LLMs are massively less intelligent than they could be, based on the amount of training data and compute they consume. Or rather, humans are orders of magnitude more efficient on training, and we don't know why
The answer is probably in the DNA. We are born highly intelligent and our brain is still growing. The DNA itself was optimized for billions of years
I think it's more accurate to say that the fundamental differences (between LLM's and humans) are abundantly clear and what we haven't really got a handle on are the similarities. Why is it that an LLM's can so convincingly mimic some things that humans do? Of course the fact that we don't really understand how humans do it in the first place is a real barrier. Perhaps looking at the intersection will shed some light on how humans work.
Machine learning is more like biology, physics or law than like CS or math.
In the software engineering world, we're accustomed to the reality that to do great work, all you need is a sharp mind and a cheap laptop. Better tools help, but aren't really a necessity. You could probably become a top-notch Linux kernel engineer with nothing but dedication and self-study, a $300 laptop and a shabby apartment with internet access.
You just can't really learn ML that way, just like you can't do it with banking, professional trading or medicine.
Simply put, I could compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist once I own the computer.
But I can't do the same for things that excite me in this DL domain, even running pretrained models requires hardware that most hobbyists can't afford.
Cloud solutions exist but unlike traditional dev where we could test almost everything about the application locally, only thing unsolved was deployment and distribution in some cases, which is a sensible spend on infrastructure.
Lot of effort and labor, not payed by commercial companies, went into your ability to compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist. For example GCC:
"
In late 1983, in an effort to bootstrap the GNU operating system, Richard Stallman asked Andrew S. Tanenbaum, the author of the Amsterdam Compiler Kit (also known as the Free University Compiler Kit), for permission to use that software for GNU. When Tanenbaum advised him that the compiler itself was not free, but rather only the university, Stallman decided to work on a different compiler.
"
Also the whole reason for existence of GNU Project:
"
According to Stallman, what prompted him to start the GNU Project was having previously had an experience with Xerox refusing to give him access to the source code of a laser printer used at the MIT Lab, now with the realization that this community of sharing software would end and noticing the advancing spread of proprietary software which had undone the culture of sharing software that had previously existed, Richard Stallman refused to adapt by continuing with proprietary software, and he instead decided that with this skills as an operating systems programmer, he would begin the development of a new free software operating system and to recreate a community of sharing software that was lost, called GNU.
"
> Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Compute is only an issue if you want to train large foundational models from scratch. You can easily train non-neural models on CPU, and fine-tune smaller models (say <4B) on a commodity GPU.
For training data there's lots of datasets online (like Kaggle), but you can build your own and can label things yourself or get a language model to label them for you. Getting good data is the hardest part of ML so this is a good exercise.
Then do some hobby projects: classify whether your photos contain a photo of you or a particular friend/family member, predict the expense category from bank transaction exports, classify your emails into categories, extract book recommendations from Hacker News, ...
Get Sebastian's book and build an LLM from scratch. The problem with most textbooks (on any subject, it's not unique to ML/AI) is they usually try to build the theoretical foundations first. In many cases things are better taught top down.
I'm OK going through the learning itself (will definitely check out the book though, since it's so well liked!). The question is more, what do you do with that once the book or course is done?
For a junior coder, there's usually a path to recommend, joining some open source project or doing some home scripting, etc. Something where you can be useful even if it's not your full time job.
I'm wondering whether there's some similar hobbyist path available here, or whether there's too much of a jump between the educational/pop sci stuff and the professional realm. If it's the second, that's also fine, I just want to know if I'm missing something.
One practical-ish thing you could reasonably do with this (beyond just better understanding how models work so you can make better choices/tradeoffs), is post-train some of the small-to-medium models, fine-tune them on your own data or priorities.
You don't need to change many weights, relatively, to add capabilities or modify behavior. Look up LoRas for instance.
I'm of the opinion that post-training existing models is an area with a lot of benefit and fun in the near future (it's already being done here and there of course).
I doubt you _really_ have to know everything in this course to know how to do that, but I'd want this as background personally for it.
You included machine learning in with LLMs in your root post. Try training a supervised model for some task, like an image classifier or a detector. LLMs have not replaced convolutional models, and even transformers can be used for non-NLP tasks. The idea this is somehow not professional is not true. Despite what people would have you believe, ResNet50 fine-tuned on a good dataset is still competent. In many environments there isn’t enough compute for bigger models, and large language models aren’t always the best choice.
You also don’t have to go straight into LLMs. You can learn most of the training mechanics via classic deep learning. It isn’t much different with LLMs except the training data and losses are specific and the models are much larger.
The comment below/above is right. Pre-training isn't the path (but you really do need to learn how it's done). Learn how it works, learn the architecture, then take models and do post training. Fine tuning and RL on them. There was a fun/clever/useless project recently posted here about building a terminal emulating model thing, as an example.
I completely understand where you're coming from. While understanding the math and architectures of these large models can be edifying, it lacks some of the liberating/empowering spirit of traditional software. You can build a bespoke to-do app that is better suited to your needs than anything produced by some productivity software company, but you're totally beholden to one of the few big players for access to an effective LLM.
But there is so, so much interesting work and research to do at a much smaller scale. I work on drug discovery models, and none of my work requires data or infrastructure that are not generally accessible to the public (it's nice to have the computing resources of my institution but there are other ways to access a GPU).
So if you are really interested in this stuff, I would suggest looking at the endless supply of interesting problems in domains that are not dominated by huge frontier labs.
Computer science turned bioinformatitian and AI engineer here. I have a BSc in Computer science since 2002 in 2013 I decided to start a Master in Bioinformatics and Computational biology and in 2018 did a PhD. Since my background is in CompSci, first before I started the master I just bought some popular science books about genetics, and biology the kind of books you see in bookstores like Barnes & Nobles, nothing too specialized. Then watched some videos about some things like Biochemistry, Genetics, DNA replication etc... then what really made a difference is taking a Mollecular Biology course, the one I took prepared me with all the pre-requisites and the book I used in the course are these : Essencial cell biology https://wwnorton.com/books/9781324033356
I feel that this book is clearly written and will give you all prerequisites to continue in a biotech, bioinformatics, computational biology or even biology graduate program if you come from a different discipline.
For genomics, look into viruses, in particular RNA viruses like influenza and HIV. You can fit the entire genome on your screen and learn to read it like code! There are some good text books about viruses that include genomics from scratch (I can't find the one I used now but could look later if you're interested). My advice would be to learn the biology first and understand why we care about genomes before looking into the computational stuff.
I have the books on my shelf for 5 years now and never encountered the need to build an AI so i haven't touched it yet. Don't feel bad about using AI to learn but not build one.
You can google stuff to learn without the need to build a search engine too.
I have however learned a lot about math! And other related stuff. I'd argue if you extend your low level skills in math and code when the need for an AI comes up, simple weather / sales forecast, who knows, you'll have an easier time building one.
To find good applicable problems you have to be inside or close to an eco system that is constantly working on these problems. Otherwise its like Napolean Dynamite trying to make Star Wars in Idaho because Uncle Rico gave him a free camera and Kip gave him an article about George Lucas. Got to move closer to Hollywood Napolean.
In my experience, until you implement the math of a NN in numpy or something similar, it's easy to feel like a library is abstracting something away from you. Many people in ML focused academia stand on those libraries. They may conceptually understand chain rule but do not manually implement the math. The same feeling exists for encryption. There is a culture of shying away from rolling out your own ops, so it can easily feel like your understanding is segmented to some degree.
I think it's one of those things where you have to work in the field to really get it and be productive. You just don't have the time or resources otherwise.
if you are willing to put in the work you can devise a hyper specialization for which you can create an RL playground (which is likely absent in the frontier labs), then you can obtain cutting edge results on it by post training an open weights model using minimal resources.
But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
That's what Karpathy's videos are for. You end up with a GPT-2.0 equivalent if you follow them to the end.
This is still pointless though. I mean it's valuable from a learning perspective but the parent is saying they want to contribute somehow. You can only learn so much from toy examples
Once you learn how an LLM works, you can likely _begin_ to contribute meaningfully to numerous open-source AI projects: inference engines, training frameworks, etc. Or try to find an area of AI that is lacking research and start your own experiments. Or (potentially) go to work for a company doing similar or adjacent work.
Opportunity knocks, but you have to build the door first.
The prereqs aren't as extensive as people fear, but they are important. Starting out by building a scratch-trained GPT3-scale model is always an option, but anyone who's going to succeed at that doesn't need to watch any YouTube videos.
For the rest of us, the sense of discovery that we get from training and running toy models is a big part of the reward. This stuff is not an exact science, at least not yet, so time spent building intuition with small-scale models is an incredibly important part of the learning process. Not pointless at all.
Several years ago I put together a classifier for putting my bank account statements into categories (groceries, housing etc). I use it for doing my accounts and budgeting. Maybe you have a similar problem? There are still lots of applications for small classifiers, you don't need a SOTA LLM for everything.
I feel the same. I feel machine learning is 1) you need strong math knowledge but also intuition. 2) You need actual production level data, a lot of the nuances of machine learning, applied scientists' roles are how to build strong models with real world, messy, inconvenient data. You need to apply various techniques and not just memorize a specific formula.
Compared to software coding, you build websites, apps etc. The barrier to entry is much higher.
I am trying to pivot into machine learning myself, so far my understanding is try to find a software engineer role where you deal with data, model infra, data pipeline, etc. Then within the same company, try to do more modeling work and build intuition and resume credentials in this area. Someone correct me if I am wrong.
Machine learning IS applied mathematics, especially statistics, probability, and optimization. Period.
The "baby steps" are not there just for intelectual satisfaction, the baby steps give you the building blocks to understand things that become progressively more complex. The baby steps are tools, that once you get familiar with them, you'll find new and new uses everywhere.
A good practical illustration of this: It is pretty hard to figure out how differential equations can be useful if you don't understand what they are. And yet, I bet that on january of 2020, there was at least one math teacher deep down in the amazonian jungle playing with a compartimental model based on a system of ordinary differential equations, to figure out the diverse scenarios this new epidemic could play out if that scary new virus escaped China and those few cruise ships.
Another example: take the concept of backpropagation: the underlying idea behind it is the chain rule from calculus, used to work backward through a computation and calculate how each parameter affects the error. Now, it is way easier to understand it in a small CNN that you are training to recognize handwritten digits than trying to understand it in the context of a full transformer model. A digit classifier is something that is way easier and affordable for you to fiddle with stuff and develop and intuition on loss functions, gradients, backpropagation and overfitting.
I feel there's a misconception in a lot of the comment threads about what an LLM-from-scratch tutorial is for. Of course you're not going to come out of this kind of tutorial being able to train a useful LLM from scratch. (It requires too much compute, and datasets you don't have access to, and there are a lot of tricks and operational problems to solve for real training runs.) If that's your bar for "impracticality", then sure, this is impractical.
But that's not the point; the point is understanding these things better by seeing how they are made (or grown, if you prefer). Building something at a level further down the stack than you will ever go forces you to confront details that shape the layers above that you do deal with. This is one reason why Crafting Interpreters is such a beloved book here on HN.
LLMs are now at the core of tools we all use everyday... don't you want to know how they are made? Maybe not everyone has this itch to understand the platforms and tools they use, but I sure do, and it's helped me over and over again. Sure, LLMs aren't like operating systems or compilers. They're a quasi-organic mass of billions of parameters learned during training and honestly nobody (not even the mech interp folks) really deeply understands why they work so well. But that's no reason to completely give up and there is still a lot you can learn.
Take RLVR ("Reinforcement Learning from Verifiable Rewards"). This is the training technique that made LLMs get so good at programming and math in 2025 and 2026. A lot of people who confidently talk about LLM capabilities either haven't heard of it (and thus end up parroting out-of-date factoids about LLM training derived from the pre-2025 era) or have only a very dim idea of what it is. In this course you actually do RL training of an LLM to get it solving math problems better. You're right down in the details. It's interesting for its own sake but it will also give you a grounding for discussing questions such as: "why did LLM writing get so weird and bad in mid-2026?" and "why is it so hard to train LLMs to be good at things that aren't like math and programming?"
Anyway, Sebastian's two books are great and highly recommended; I worked through the first one a year ago and it was one of the best investments of my time in all of 2025. If you prefer video for learning this is great as well (although I'd still say - try the books first).
personally I think it's because of models trained so hard to be good at Math and Coding with reinforcement learning, which makes them kind of crazy hard reasoners, and bad for casual discussion
I got his book Build Your Own LLM From Scratch and it's super readable and does a great job at holding your hand through the entire process...need to finish reading it though!
I did the same. I got "Build Your Own LLM From Scratch" last christmas and it is a great resource for learning how LLMs work, from tokenization to generating text. The author makes a great job explaining each new concept. If you know PyTorch (or at least know about tensors), the material will be approachable for you.
Sebastian, if you are reading this, this is an idea for another book: how to orchestrate agents for adversarial code review. I've been looking for learning resources to make something similar to this [1] at a smaller scale. I am aware of codex subagents, though. However, given that Sebastian already has a book for LLMs and another book on planning, I think adversarial code review is an interesting next step.
I don't like working with neural networks and I always found everything overcomplicated or oversimplified, sometimes both at the same time.
But this book is almost perfect. Every new concept is well described and every is well founded. And every step moves you forward (if you know, you know)
I like it too. Haven't got very far through it yet but just learning about how to use PyTorch and finally getting how back propagation works via autograd etc. was really fun and enlightening.
What would be the computing cost to go through the book (or the course)? Do you need Cloud GPUs, or can this be run locally, if so how powerful your computer has to be?
I've been wondering about getting a PC to poke around LLM stuff (not a practical use, but more for learning purposes) but I don't have any intuition about how much is enough and whether it is worth it to me.
You can go through it locally just fine; all you need to do is be able to run Qwen3 0.6B, which is a tiny LLM. A recent macbook (air or pro), or a PC with a good gaming GPU is perfect, but you can run the model and training examples on a PC CPU too; it will just be much slower. From his book (same examples as the video course):
> "the code examples have been carefully designed to run efficiently on a regular laptop, without the need for any special hardware. But if you do have access to a GPU, certain sections provide helpful tips on scaling up the datasets and models to take advantage of that extra power... The inference and evaluation chapters work well on CPUs and GPUs, while the training-oriented chapters benefit from access to a GPU if you want to reproduce the larger experiments."
So, consumer GPU helps but not essential. No need for expensive cloud GPU time.
I’ve read, worked through, and watched his LLM book as well as this one! Things finally clicked, can’t recommend both books enough—his pedagogy is great (at least for me).
All of these build your own model guides are like someone putting out a build your own CPU guide 10 years ago. Interesting, but not of any practical use.
I was recently part of “AI layoffs” and had to do an interview circuit. All my interviews asked these, as well as efficient inference topics from How To Scale Your Model.
> A reasoning model is essentially an upgrade from a conventional LLM, that is, adding more post-training via reinforcement learning with verifiable rewards (and optionally inference scaling techniques) to make it more capable of solving more challenging problems.
But this isn't really true is it?
When reasoning models came around with OpenAI's o1, the main feature it had was to be able to try several paths and select the best one. This is what a reasoning model is. This was before agents and reinforcement learning with verifiable rewards wasn't it?
And then RLVR and agents were added on later. Or am I wrong?
I just started reading his book, and he makes it easy to grasp difficult concepts. As a matter of fact, I have been working on platform where you can learn how AI models work in an interactive manner.
This is an essential resource and his book is a must read from this author. I have the upmost respect for Raschka for his research and detailed explanations in AI including him dissecting research papers.
I'm a bit confused as to who this is for - apart from learning how the tech works that is.
If you're truly going to build your own model (and commit to the cost of that), then video tutorials will not cut it.
And if you don't really, then I wonder if this is really the best style of conveying the information you'd be interested in?
I mean I might be wrong and maybe this helps emerging players or something like that, but to me it is more likely that this is more of a monetisation strategy of a personal brand. Living off selling to people that think they _should_ but then in the end don't.
Why teach people to play guitar once a week if they won't be a new Jimi Hendrix? Why teach people to run if they won't be a new Usain Bolt?
Maybe because they don't want to be an "emerging player" or something, they only want to understand how a new technology that is changing the world works and prefer to learn by doing.
If Sebastian is doing it by its personal brand, he really deserves it. I would trust him because I know he is a reference on his area of knowledge and I know he is because he shares his knowledge instead of only making fancy talks.
Let’s not be this negative, we might be going towards a new direction with technology that could allow small teams to build their own models. Maybe not frontier models. Maybe not close to the others in generalized benchmarks. But perhaps they could be really specialized into solving one particular problem better than the others.
If you're truly going to build your own model (and commit to the cost of that), then video tutorials will not cut it.
That makes no sense. It's like saying if you want an engineering degree, going to lectures will not cut it. Of course not, you have to do the homework and projects too.
You're right about me not being the target audience, but you're wrong about the second sentence I think.
The target audience of this is for people that like to _think_ they're doing stuff by paying for it, to then not do anything.
It's a purchase of an identity, basically.
Which.. I guess is fine as something to cover Sebastian's expenses to enable him to keep delivering this free stuff. If you want to take money from someone, taking from this group is likely the most ethical thing to do.
"I know I'm not the target audience and I don't have any interest in how things work, but let me tell you how you're wrong by telling you who the actual target audience is"
What's next? Are you going to give us your opinion of music genres you don't ever listen to? And tell us who should listen to them too, I suppose.
You are completely off base. His initial book was released almost two years ago. I post train models, for work, and I learned more from his book on LLMs than any other source on LLMs. There is a lot more to it than that book, but you have to start somewhere, even if you have a decent theoretical foundation. There are a bunch of people who learned how to build them from his book.
How do you think people learn to build/train models? It comes down from the sky?
That is a good counter (and the only interesting one in this subthread for that matter).
My core point I think is "first principles or nothing", but, thinking about what you said, I could potentially be off there.
I might be lacking the connection to and understanding of academia and how research and work gets done there. Maybe it actually isn't possible to do this work that way.
I doubt it. Strongly.
But this is a valid counterpoint. (Which I had to kinda come up with myself, but anyway)
You sound like you are projecting a bunch of motivations and assumptions about character onto other people. But you projecting such things onto another person is a terrible way to begin to understand something.
There are genuinely people who enjoy learning how something works by doing smaller scale versions of it themselves. This site happens to have a much higher average percentage of them than most other places.
People are going to be upset about this, but any learning material in this day and age is solely made for your own understanding when writing it. The LLMs can be better teachers and understand your level of need and engagement better than anyone else.
I've got a question. I'm having a bit of trouble phrasing it, so please don't assume I'm against learning for learning's sake, that's not the point.
Whenever I've tried to learn about LLMs, machine learning, etc. I always come out with a bad feeling, compared to learning about software.
Theoretical courses make me feel like it's all baby steps, unless you assume a level of commitment equivalent to a math degree. Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Now this feeling of "impracticality" is perfectly fine. It would be the expected outcome if I was taking a course on classic literature or modern art. But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
Did anyone have the same experience? any tips if so?
I think this is an outcome of playing with systems that we don’t actually understand very well on a deep level. We have found recipes that work, and have used graduate descent (so many PhD theses) to understand and optimize these systems, but we’ve built a model of language that also encodes reasoning, and those things are extraordinarily complex. Providing proper mathematical rigor for their explanation requires, as you say, the level of commitment equivalent to a math degree. Also, because language is so complicated and subtle, you need highly precise statistical models of it, and that just takes a shitload of data; hence the scale. You can train toy models at home, but they will suck because you actually need massive amounts of data to capture the nuance that language requires. And that’s to say nothing of multimodal models.
I think the level of interaction on forums like HN is a great example of the still-yawning gulf between LLMs and humans when it comes to encoding reasoning into language. Even with the self-selection that comes with posting here, think of how many times it is almost impossible to tell if posters are shit-posting, serious, angry, sock puppets, or just weird.
You say "...because you actually need massive amounts of data to capture the nuance that language requires..." but humans don't require that! So why do LLMs?
How many books did you read in your education? Far fewer that what LLMs read, no doubt. Of course we "have a life" and so we interact with hundreds of people, which is also a form of learning.
But in any case there's are fundamental differences here between these two systems (LLMs and humans) that we have not yet discerned clearly.
Far fewer than an LLM but then I “know” far less than an LLM. Sam Altman talked about a PhD level expert on any topic; if we expect a SOTA model to answer any question on any topic in a library, presumably that requires a library worth of books go into it?
Say I ask it who are the most beloved Indian cricketers and why, it might not be correct but it will have the right vocabulary - cricket terms and gameplay I’ve not heard of, player names and styles I don’t recognise, things critics and commentators like and dislike about game strategies. And it is like that for almost any topic.
If they hallucinate a Python function, it will look like a plausible function e.g. “hn.create_user_account()”. They don’t hallucinate like
It’s a terrific amount of facts to be able to write even plausibly about tons of topics.We don't need LLMs that can "answer any question on any topic in a library"!
We don't even need machines that can answer any question in a specific topic area. But we could use machines that can answer "I don't know." when they cannot compute the answer.
Consider the possibility of much-smaller LLMs that answer questions only in limited domains (physics, chemistry, math, medicine, etc.)?
Could not such LLMs exist? If not, why not? Would they not likely be more useful and far, far easier and cheaper to create and run (possibly even with far less hardware) than the current "know-all" LLMs? Also might they possibly avoid certain problems ( e.g., hallucinations, deceit, apparently undesirable behavior, etc.) associated with the "know-all" LLMs we now have?
I feel the intuition leads us astray here because the combined text of every science book ever written is much less than the size of a complete description of the human brain. To the hamburger of reason the library is a sesame seed.
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We need to up the ante, we all know it's the Mumbai Indians. ;)
One can train a decent model at home, for a specific task but you are absolutely correct that in order to have a model that's an expert in any topic it requires training and data on all topics.
I nearly spit out coffee reading this. Amazing use of vocabulary; especially "... oh great spirit of the internet".
I sometimes jokingly explain to certain people not immersed in tech that sometimes saying the "magic words" (commands) from the "codex" (script) in the right sequence with a lot of hand waving allow "magic" (applications/web apps/etc.) to happen.
It generally brightens a patients day and makes things relatable to those non-technical, children, the elderly, or the like.
TLDR: thanks for the chuckle.
We have DNA though. It was subject to "pretraining" for billions of years. Alongside some epigenetic stuff
Maybe in the future we will have fewer robots interacting in the environment with continuous learning, to mimic the human experience, rather than a massive fleet of stateless, inference-only models
Humans don’t need that? How many words have you taken in from all possible sources by the time you’re 18? And many of those words came with additional data due to tone of voice, inflection and context. And then some of those (in-person, filmed art) came with incredibly subtle data from body language and facial expressions. I think we take in a massive corpus of data every year. Think just of any book you re-read: why did you do it? Did you glean additional insight?
We experience tons of data, but it's still much less than the pretraining step of LLMs
I think it's clear that LLMs are massively less intelligent than they could be, based on the amount of training data and compute they consume. Or rather, humans are orders of magnitude more efficient on training, and we don't know why
The answer is probably in the DNA. We are born highly intelligent and our brain is still growing. The DNA itself was optimized for billions of years
How have you quantified this that you are asserting it to me?
I think it's more accurate to say that the fundamental differences (between LLM's and humans) are abundantly clear and what we haven't really got a handle on are the similarities. Why is it that an LLM's can so convincingly mimic some things that humans do? Of course the fact that we don't really understand how humans do it in the first place is a real barrier. Perhaps looking at the intersection will shed some light on how humans work.
Machine learning is more like biology, physics or law than like CS or math.
In the software engineering world, we're accustomed to the reality that to do great work, all you need is a sharp mind and a cheap laptop. Better tools help, but aren't really a necessity. You could probably become a top-notch Linux kernel engineer with nothing but dedication and self-study, a $300 laptop and a shabby apartment with internet access.
You just can't really learn ML that way, just like you can't do it with banking, professional trading or medicine.
Weird to put banking and trading on the same complexity level as medicine or ML.
Agreed trading and banking are social systems, more complex that biological systems
Kind of sounds like a truism. You make the claim, but you don't explain why. Also, the analogies at the end are... odd.
Maybe you are right! I'd like to hear the justifications more though.
Simply put, I could compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist once I own the computer.
But I can't do the same for things that excite me in this DL domain, even running pretrained models requires hardware that most hobbyists can't afford.
Cloud solutions exist but unlike traditional dev where we could test almost everything about the application locally, only thing unsolved was deployment and distribution in some cases, which is a sensible spend on infrastructure.
Lot of effort and labor, not payed by commercial companies, went into your ability to compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist. For example GCC:
" In late 1983, in an effort to bootstrap the GNU operating system, Richard Stallman asked Andrew S. Tanenbaum, the author of the Amsterdam Compiler Kit (also known as the Free University Compiler Kit), for permission to use that software for GNU. When Tanenbaum advised him that the compiler itself was not free, but rather only the university, Stallman decided to work on a different compiler. "
https://en.wikipedia.org/wiki/GCC
Also the whole reason for existence of GNU Project:
" According to Stallman, what prompted him to start the GNU Project was having previously had an experience with Xerox refusing to give him access to the source code of a laser printer used at the MIT Lab, now with the realization that this community of sharing software would end and noticing the advancing spread of proprietary software which had undone the culture of sharing software that had previously existed, Richard Stallman refused to adapt by continuing with proprietary software, and he instead decided that with this skills as an operating systems programmer, he would begin the development of a new free software operating system and to recreate a community of sharing software that was lost, called GNU. "
https://en.wikipedia.org/wiki/GNU
> Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Compute is only an issue if you want to train large foundational models from scratch. You can easily train non-neural models on CPU, and fine-tune smaller models (say <4B) on a commodity GPU.
For training data there's lots of datasets online (like Kaggle), but you can build your own and can label things yourself or get a language model to label them for you. Getting good data is the hardest part of ML so this is a good exercise.
Then do some hobby projects: classify whether your photos contain a photo of you or a particular friend/family member, predict the expense category from bank transaction exports, classify your emails into categories, extract book recommendations from Hacker News, ...
Get Sebastian's book and build an LLM from scratch. The problem with most textbooks (on any subject, it's not unique to ML/AI) is they usually try to build the theoretical foundations first. In many cases things are better taught top down.
I'm OK going through the learning itself (will definitely check out the book though, since it's so well liked!). The question is more, what do you do with that once the book or course is done?
For a junior coder, there's usually a path to recommend, joining some open source project or doing some home scripting, etc. Something where you can be useful even if it's not your full time job.
I'm wondering whether there's some similar hobbyist path available here, or whether there's too much of a jump between the educational/pop sci stuff and the professional realm. If it's the second, that's also fine, I just want to know if I'm missing something.
One practical-ish thing you could reasonably do with this (beyond just better understanding how models work so you can make better choices/tradeoffs), is post-train some of the small-to-medium models, fine-tune them on your own data or priorities.
You don't need to change many weights, relatively, to add capabilities or modify behavior. Look up LoRas for instance.
I'm of the opinion that post-training existing models is an area with a lot of benefit and fun in the near future (it's already being done here and there of course).
I doubt you _really_ have to know everything in this course to know how to do that, but I'd want this as background personally for it.
You included machine learning in with LLMs in your root post. Try training a supervised model for some task, like an image classifier or a detector. LLMs have not replaced convolutional models, and even transformers can be used for non-NLP tasks. The idea this is somehow not professional is not true. Despite what people would have you believe, ResNet50 fine-tuned on a good dataset is still competent. In many environments there isn’t enough compute for bigger models, and large language models aren’t always the best choice.
You also don’t have to go straight into LLMs. You can learn most of the training mechanics via classic deep learning. It isn’t much different with LLMs except the training data and losses are specific and the models are much larger.
The comment below/above is right. Pre-training isn't the path (but you really do need to learn how it's done). Learn how it works, learn the architecture, then take models and do post training. Fine tuning and RL on them. There was a fun/clever/useless project recently posted here about building a terminal emulating model thing, as an example.
In many cases if you follow a recipe without understanding you're no better off in the end. In cooking at least you can have a cake and eat it too.
i am actually looking for a book that follows like sebastian books but without any hands on coding . i am hoping to read in bed.
There's a note in Sebastian Raschka's LLMs-from-scratch github repository - "Recommendations for Getting the Most Out of a Technical Book".
He recommends reading offline and code-free first (although chapter-by-chapter).
https://github.com/rasbt/LLMs-from-scratch/blob/cdbd33e6d57a...
Ask and... ye shall receive! Check out `Hands-On Large Language Models: Language Understanding and Generation`.
...I wanna mention this book as well, although it doesn't fit the criteria: https://www.thelittlelearner.com[
You don't need to code to follow it, just read along
I read it in a cabin on a lake, far from any internet access, and without a computer. I still found it quite useful.
I completely understand where you're coming from. While understanding the math and architectures of these large models can be edifying, it lacks some of the liberating/empowering spirit of traditional software. You can build a bespoke to-do app that is better suited to your needs than anything produced by some productivity software company, but you're totally beholden to one of the few big players for access to an effective LLM.
But there is so, so much interesting work and research to do at a much smaller scale. I work on drug discovery models, and none of my work requires data or infrastructure that are not generally accessible to the public (it's nice to have the computing resources of my institution but there are other ways to access a GPU).
So if you are really interested in this stuff, I would suggest looking at the endless supply of interesting problems in domains that are not dominated by huge frontier labs.
Ive been interested in exploring other domains as well, including biology, genomics, etc. Do you have any recommendations on where to get started?
Computer science turned bioinformatitian and AI engineer here. I have a BSc in Computer science since 2002 in 2013 I decided to start a Master in Bioinformatics and Computational biology and in 2018 did a PhD. Since my background is in CompSci, first before I started the master I just bought some popular science books about genetics, and biology the kind of books you see in bookstores like Barnes & Nobles, nothing too specialized. Then watched some videos about some things like Biochemistry, Genetics, DNA replication etc... then what really made a difference is taking a Mollecular Biology course, the one I took prepared me with all the pre-requisites and the book I used in the course are these : Essencial cell biology https://wwnorton.com/books/9781324033356
I feel that this book is clearly written and will give you all prerequisites to continue in a biotech, bioinformatics, computational biology or even biology graduate program if you come from a different discipline.
Good luck!
For genomics, look into viruses, in particular RNA viruses like influenza and HIV. You can fit the entire genome on your screen and learn to read it like code! There are some good text books about viruses that include genomics from scratch (I can't find the one I used now but could look later if you're interested). My advice would be to learn the biology first and understand why we care about genomes before looking into the computational stuff.
I have the books on my shelf for 5 years now and never encountered the need to build an AI so i haven't touched it yet. Don't feel bad about using AI to learn but not build one.
You can google stuff to learn without the need to build a search engine too.
I have however learned a lot about math! And other related stuff. I'd argue if you extend your low level skills in math and code when the need for an AI comes up, simple weather / sales forecast, who knows, you'll have an easier time building one.
To find good applicable problems you have to be inside or close to an eco system that is constantly working on these problems. Otherwise its like Napolean Dynamite trying to make Star Wars in Idaho because Uncle Rico gave him a free camera and Kip gave him an article about George Lucas. Got to move closer to Hollywood Napolean.
Yes. That's why I'm studying a Master on AI!!
But you know what? There's a little secret. The whole bunch of tech consultants will throw everything and that,s all friends. And sadly, it will work.
Also, I believe that there are many bottlenecks because many parte are written in Python and by not so good programemos where you and ohters can shine
In my experience, until you implement the math of a NN in numpy or something similar, it's easy to feel like a library is abstracting something away from you. Many people in ML focused academia stand on those libraries. They may conceptually understand chain rule but do not manually implement the math. The same feeling exists for encryption. There is a culture of shying away from rolling out your own ops, so it can easily feel like your understanding is segmented to some degree.
I think it's one of those things where you have to work in the field to really get it and be productive. You just don't have the time or resources otherwise.
if you are willing to put in the work you can devise a hyper specialization for which you can create an RL playground (which is likely absent in the frontier labs), then you can obtain cutting edge results on it by post training an open weights model using minimal resources.
This.
Don't try to pretrain from scratch. Take an already-trained Qwen and give it new powers.
It may be inherent. Perhaps comparable to understanding/building a practical CPU.
Try making a smaller classifier model for some random problem you have that's immediately useful and doesn't exist yet.
But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
That's what Karpathy's videos are for. You end up with a GPT-2.0 equivalent if you follow them to the end.
This is still pointless though. I mean it's valuable from a learning perspective but the parent is saying they want to contribute somehow. You can only learn so much from toy examples
Once you learn how an LLM works, you can likely _begin_ to contribute meaningfully to numerous open-source AI projects: inference engines, training frameworks, etc. Or try to find an area of AI that is lacking research and start your own experiments. Or (potentially) go to work for a company doing similar or adjacent work.
Opportunity knocks, but you have to build the door first.
The prereqs aren't as extensive as people fear, but they are important. Starting out by building a scratch-trained GPT3-scale model is always an option, but anyone who's going to succeed at that doesn't need to watch any YouTube videos.
For the rest of us, the sense of discovery that we get from training and running toy models is a big part of the reward. This stuff is not an exact science, at least not yet, so time spent building intuition with small-scale models is an incredibly important part of the learning process. Not pointless at all.
Several years ago I put together a classifier for putting my bank account statements into categories (groceries, housing etc). I use it for doing my accounts and budgeting. Maybe you have a similar problem? There are still lots of applications for small classifiers, you don't need a SOTA LLM for everything.
I feel the same. I feel machine learning is 1) you need strong math knowledge but also intuition. 2) You need actual production level data, a lot of the nuances of machine learning, applied scientists' roles are how to build strong models with real world, messy, inconvenient data. You need to apply various techniques and not just memorize a specific formula.
Compared to software coding, you build websites, apps etc. The barrier to entry is much higher.
I am trying to pivot into machine learning myself, so far my understanding is try to find a software engineer role where you deal with data, model infra, data pipeline, etc. Then within the same company, try to do more modeling work and build intuition and resume credentials in this area. Someone correct me if I am wrong.
Machine learning IS applied mathematics, especially statistics, probability, and optimization. Period.
The "baby steps" are not there just for intelectual satisfaction, the baby steps give you the building blocks to understand things that become progressively more complex. The baby steps are tools, that once you get familiar with them, you'll find new and new uses everywhere.
A good practical illustration of this: It is pretty hard to figure out how differential equations can be useful if you don't understand what they are. And yet, I bet that on january of 2020, there was at least one math teacher deep down in the amazonian jungle playing with a compartimental model based on a system of ordinary differential equations, to figure out the diverse scenarios this new epidemic could play out if that scary new virus escaped China and those few cruise ships.
Another example: take the concept of backpropagation: the underlying idea behind it is the chain rule from calculus, used to work backward through a computation and calculate how each parameter affects the error. Now, it is way easier to understand it in a small CNN that you are training to recognize handwritten digits than trying to understand it in the context of a full transformer model. A digit classifier is something that is way easier and affordable for you to fiddle with stuff and develop and intuition on loss functions, gradients, backpropagation and overfitting.
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I feel there's a misconception in a lot of the comment threads about what an LLM-from-scratch tutorial is for. Of course you're not going to come out of this kind of tutorial being able to train a useful LLM from scratch. (It requires too much compute, and datasets you don't have access to, and there are a lot of tricks and operational problems to solve for real training runs.) If that's your bar for "impracticality", then sure, this is impractical.
But that's not the point; the point is understanding these things better by seeing how they are made (or grown, if you prefer). Building something at a level further down the stack than you will ever go forces you to confront details that shape the layers above that you do deal with. This is one reason why Crafting Interpreters is such a beloved book here on HN.
LLMs are now at the core of tools we all use everyday... don't you want to know how they are made? Maybe not everyone has this itch to understand the platforms and tools they use, but I sure do, and it's helped me over and over again. Sure, LLMs aren't like operating systems or compilers. They're a quasi-organic mass of billions of parameters learned during training and honestly nobody (not even the mech interp folks) really deeply understands why they work so well. But that's no reason to completely give up and there is still a lot you can learn.
Take RLVR ("Reinforcement Learning from Verifiable Rewards"). This is the training technique that made LLMs get so good at programming and math in 2025 and 2026. A lot of people who confidently talk about LLM capabilities either haven't heard of it (and thus end up parroting out-of-date factoids about LLM training derived from the pre-2025 era) or have only a very dim idea of what it is. In this course you actually do RL training of an LLM to get it solving math problems better. You're right down in the details. It's interesting for its own sake but it will also give you a grounding for discussing questions such as: "why did LLM writing get so weird and bad in mid-2026?" and "why is it so hard to train LLMs to be good at things that aren't like math and programming?"
Anyway, Sebastian's two books are great and highly recommended; I worked through the first one a year ago and it was one of the best investments of my time in all of 2025. If you prefer video for learning this is great as well (although I'd still say - try the books first).
Why did LLM writing get so weird and bad in mid-2026?
personally I think it's because of models trained so hard to be good at Math and Coding with reinforcement learning, which makes them kind of crazy hard reasoners, and bad for casual discussion
put your prompt instead of your ai output so we can see the bias
I got his book Build Your Own LLM From Scratch and it's super readable and does a great job at holding your hand through the entire process...need to finish reading it though!
I did the same. I got "Build Your Own LLM From Scratch" last christmas and it is a great resource for learning how LLMs work, from tokenization to generating text. The author makes a great job explaining each new concept. If you know PyTorch (or at least know about tensors), the material will be approachable for you.
Sebastian, if you are reading this, this is an idea for another book: how to orchestrate agents for adversarial code review. I've been looking for learning resources to make something similar to this [1] at a smaller scale. I am aware of codex subagents, though. However, given that Sebastian already has a book for LLMs and another book on planning, I think adversarial code review is an interesting next step.
[1] https://bun.com/blog/bun-in-rust
[2] https://learn.chatgpt.com/docs/agent-configuration/subagents...
Agreed - it's a great first book. I followed it up with his "Build a Reasoning Model from Scractch" and that was a great second step.
I don't like working with neural networks and I always found everything overcomplicated or oversimplified, sometimes both at the same time.
But this book is almost perfect. Every new concept is well described and every is well founded. And every step moves you forward (if you know, you know)
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I read it the first time with a study group which was very helpful.
I like it too. Haven't got very far through it yet but just learning about how to use PyTorch and finally getting how back propagation works via autograd etc. was really fun and enlightening.
Highly recommended! Sebastian does a great job explaining complex AI topics in a very intuitive way.
Karpathy 2.0!
They are contemporaries - cs231n and Sebastian's first ML book came out in 2015 - both excellent!
Other way around. Raschka's been doing this over a decade.
Karpathy also. CS231n first came out in 2015.
Karpathy might be Raschka 2.0
What would be the computing cost to go through the book (or the course)? Do you need Cloud GPUs, or can this be run locally, if so how powerful your computer has to be?
I've been wondering about getting a PC to poke around LLM stuff (not a practical use, but more for learning purposes) but I don't have any intuition about how much is enough and whether it is worth it to me.
You can go through it locally just fine; all you need to do is be able to run Qwen3 0.6B, which is a tiny LLM. A recent macbook (air or pro), or a PC with a good gaming GPU is perfect, but you can run the model and training examples on a PC CPU too; it will just be much slower. From his book (same examples as the video course):
> "the code examples have been carefully designed to run efficiently on a regular laptop, without the need for any special hardware. But if you do have access to a GPU, certain sections provide helpful tips on scaling up the datasets and models to take advantage of that extra power... The inference and evaluation chapters work well on CPUs and GPUs, while the training-oriented chapters benefit from access to a GPU if you want to reproduce the larger experiments."
So, consumer GPU helps but not essential. No need for expensive cloud GPU time.
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I’ve read, worked through, and watched his LLM book as well as this one! Things finally clicked, can’t recommend both books enough—his pedagogy is great (at least for me).
All of these build your own model guides are like someone putting out a build your own CPU guide 10 years ago. Interesting, but not of any practical use.
You can take any open source model and continue training it. It's nothing like building a CPU.
They’re amazing for interview prep! ;)
which interviews are asking these ?
I was recently part of “AI layoffs” and had to do an interview circuit. All my interviews asked these, as well as efficient inference topics from How To Scale Your Model.
Were they for BE and related roles? Or going into inference?
Doing some prep for interviews.
Your comment doesnt answer my question :)
Maybe someone can clarify this:
He says:
> A reasoning model is essentially an upgrade from a conventional LLM, that is, adding more post-training via reinforcement learning with verifiable rewards (and optionally inference scaling techniques) to make it more capable of solving more challenging problems.
But this isn't really true is it? When reasoning models came around with OpenAI's o1, the main feature it had was to be able to try several paths and select the best one. This is what a reasoning model is. This was before agents and reinforcement learning with verifiable rewards wasn't it? And then RLVR and agents were added on later. Or am I wrong?
I just started reading his book, and he makes it easy to grasp difficult concepts. As a matter of fact, I have been working on platform where you can learn how AI models work in an interactive manner.
The way he explained the attention module helped me a lot. I have read other people explanation but his technique helped me 8n remembering it easily.
It seems going forward AI is just going to build more AI and no human will understand it.
This is an essential resource and his book is a must read from this author. I have the upmost respect for Raschka for his research and detailed explanations in AI including him dissecting research papers.
I have read both and the are excellent
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someone's getting auction-poached at openai and anthropic
he's a well known author on LLMs already.
Do you not just say “do reasoning” and leave it an open “<reasoning>” tag?
I'm a bit confused as to who this is for - apart from learning how the tech works that is.
If you're truly going to build your own model (and commit to the cost of that), then video tutorials will not cut it.
And if you don't really, then I wonder if this is really the best style of conveying the information you'd be interested in?
I mean I might be wrong and maybe this helps emerging players or something like that, but to me it is more likely that this is more of a monetisation strategy of a personal brand. Living off selling to people that think they _should_ but then in the end don't.
You can follow his book and learn how to build an LLM from scratch. What is the issue?
It's also not a bad foundation for people who want to understand how these things are trained and work.
>apart from learning how the tech works that is.
some people enjoy learning how the tech works
Like me!
"apart from learning how to make cookies at home, I really don't see the point of this cookie recipe"
"Why make things by my own hand when I can buy them cheap?!".
Why teach people to play guitar once a week if they won't be a new Jimi Hendrix? Why teach people to run if they won't be a new Usain Bolt?
Maybe because they don't want to be an "emerging player" or something, they only want to understand how a new technology that is changing the world works and prefer to learn by doing.
If Sebastian is doing it by its personal brand, he really deserves it. I would trust him because I know he is a reference on his area of knowledge and I know he is because he shares his knowledge instead of only making fancy talks.
Let’s not be this negative, we might be going towards a new direction with technology that could allow small teams to build their own models. Maybe not frontier models. Maybe not close to the others in generalized benchmarks. But perhaps they could be really specialized into solving one particular problem better than the others.
You wrote so many words to say .. nothing of substance .. i still have no clue what you are criticizing.
I think you actually do and that is exactly what resulted in this style of reply.
It's a critique of the people and the lack of substance. But not of the author. The author is highly capable.
If you're truly going to build your own model (and commit to the cost of that), then video tutorials will not cut it.
That makes no sense. It's like saying if you want an engineering degree, going to lectures will not cut it. Of course not, you have to do the homework and projects too.
Yeah but if it is not all that useful, why is it saying that it is "from scratch" and what is it doing on #1 of the front page?
Something is off here. It indeed makes no sense
Then you're not the target audience. This is intended for people who like learning things.
You're right about me not being the target audience, but you're wrong about the second sentence I think.
The target audience of this is for people that like to _think_ they're doing stuff by paying for it, to then not do anything.
It's a purchase of an identity, basically.
Which.. I guess is fine as something to cover Sebastian's expenses to enable him to keep delivering this free stuff. If you want to take money from someone, taking from this group is likely the most ethical thing to do.
"I know I'm not the target audience and I don't have any interest in how things work, but let me tell you how you're wrong by telling you who the actual target audience is"
What's next? Are you going to give us your opinion of music genres you don't ever listen to? And tell us who should listen to them too, I suppose.
You are completely off base. His initial book was released almost two years ago. I post train models, for work, and I learned more from his book on LLMs than any other source on LLMs. There is a lot more to it than that book, but you have to start somewhere, even if you have a decent theoretical foundation. There are a bunch of people who learned how to build them from his book.
How do you think people learn to build/train models? It comes down from the sky?
That is a good counter (and the only interesting one in this subthread for that matter).
My core point I think is "first principles or nothing", but, thinking about what you said, I could potentially be off there.
I might be lacking the connection to and understanding of academia and how research and work gets done there. Maybe it actually isn't possible to do this work that way.
I doubt it. Strongly. But this is a valid counterpoint. (Which I had to kinda come up with myself, but anyway)
It seems that the videos are free to access so I don't understand this comment. You think he's getting rich off selling the accompanying book?
No definitely not. You don't get rich with substance in this economy.
He is covering expenses. Probably barely.
You can't even stick by the same opinion from one comment to the next.
It seems that you want to oppose something really badly today, but you're struggling to find something that makes sense for more than 5 minutes.
Is that so?
You sound like you are projecting a bunch of motivations and assumptions about character onto other people. But you projecting such things onto another person is a terrible way to begin to understand something.
There are genuinely people who enjoy learning how something works by doing smaller scale versions of it themselves. This site happens to have a much higher average percentage of them than most other places.
People are going to be upset about this, but any learning material in this day and age is solely made for your own understanding when writing it. The LLMs can be better teachers and understand your level of need and engagement better than anyone else.