Efficient Inference
KV cache · FlashAttention · quantization
Serving a model is an engineering problem with a bill attached. Most curricula stop at training and leave you to discover the bill in production.
The math is
not deep
It is the same six tools, used over and over, for twenty-seven chapters. Nobody ever made them cheap enough to reach for. That is the entire problem, and it has nothing to do with how smart you are.
Obvix Learn is a free curriculum that runs from those six tools to reading a model's internals. You write the algorithms. A real test suite grades them in your browser.
Free · No account · Nothing leaves your machine
The gap
You finished the videos. You can fine-tune a model and ship an endpoint. Then you open an arxiv paper and the whole thing slides off your brain.
Almost everyone concludes the same thing here, and it is almost always wrong. They decide the field is above them. It isn't. Read fifteen papers and you will meet the same handful of operations every time: matrix products, dot products, derivatives, softmax, sums, argmin. That is most of the math in most of the papers.
What you are missing is not intelligence. It is fluency. The operations never became automatic, so every paper costs you full attention on the notation and leaves nothing for the idea.
“If you can multiply two matrices in your head for shape correctness, write the chain rule for f(g(x)) without looking it up, this chapter is a 90-minute skim. If those words feel slippery, this is the chapter where they stop being slippery.”
It is not just you
Someone studied 221 online courses and counted who actually reached the end. The median course finished 12.6% of the people who enrolled.
Quitting is the normal outcome, not the shameful exception. But look at the spread before drawing the wrong conclusion from that: the best course in the study kept more than half its students, and the worst kept fewer than one in a hundred. Seventy-fold difference. Finishing is something a course is designed to produce or designed to lose.
0.7%worst
12.6%median
52.1%best
The easy read is that the people who quit were not serious. Here is who is actually writing these posts.
Finished Andrew Ng's Machine Learning course
“even after finishing it, i don't feel very confident in applying the skills”
Completed Andrew Ng's ML Specialization, third-year B.Tech
“I understand the theory very well, but I struggle to apply it when building projects”
Working from Stanford's CS229 lecture notes
“I did not really struggle with the theory part but when it comes to implementing it I am not able to do it without assistance”
AI engineer with internship experience
“constantly following guides instead of building real projects or mastering the deeper concepts”
Wants to publish research
“when I pick up state-of-the-art papers, I just don't understand a single thing”
Builds things for a living
“I've never successfully built something just from a paper — I usually get stuck because either the paper is too abstract or there's not enough implementation detail”
r/learnmachinelearning · every quote links to its thread
Tutorial hell
The inability of a student to produce anything useful beyond what is taught to them — cycling through video after video, copying code line by line, and still unable to build without a tutorial open in the next tab.
“What I cannot create, I do not understand.”
The lab
Every chapter ends with code you have to make work
A real CPython interpreter and a real pytest suite run inside the page. Not a sandbox that pretends, and not a video of someone else's terminal. You fill in the function, press Run, and find out.
When a check fails it names the concept you got wrong, rather than printing a bare assertion and leaving you to guess. The first lab is two one-line functions and takes about five minutes.
- Runs on
- Pyodide + pytest
- Install
- None
- Account
- None
- Your code
- Never leaves the browser
1 def sigmoid(z): 2 # TODO: one line 3 return z
$ pytest lab01.py
FAILED test_sigmoid_is_bounded
sigmoid(20) returned 20. A squashing function must map every
real number into (0, 1) — yours is still linear.
1 failed, 1 passed
The failing check names the concept, not the assertion
The syllabus
27 chapters, one continuous line
No module is optional and none of them assume a course you were supposed to take somewhere else. Chapter 00 starts at the notation.
Foundations
00 — 08Foundations
What are the six tools, and can you reach for them without thinking?
- 00Math & Python Prereqs
- 01The ML Landscape
- 02End-to-End ML Project
- 03Classification
- 04Training Models
- 05Trees, SVMs, Kernels
- 06Ensemble Methods
- 07Dimensionality Reduction
- 08Unsupervised Learning
Deep learning
09 — 16Deep learning
Can you build the architecture everyone cites, without importing it?
- 09Intro to Neural Networks
- 10PyTorch Foundations
- 11Training Deep Networks
- 12CNNs & Computer Vision
- 13Sequences & Time Series
- 14NLP with RNNs + Attention
- 15Transformers from Scratch
- 16Multimodal Transformers
Frontier
17 — 26Frontier
Can you read what the model is doing, and prove it to someone else?
- 17Efficient InferenceSignature
- 18Generative Models
- 19RL + RLHFSignature
- 20Agents & Tool Use
- 21RAG & Vector Stores
- 22Mechanistic InterpretabilitySignature
- 23Eval Science
- 24AI Safety & Red-TeamSignature
- 25MLOps & Observability
- 26Reading Papers
Four chapters that do not exist elsewhere at this level
RL + RLHF
MDPs · PPO · DPO · reward hacking
The stage that decides how a model behaves, and the one most likely to teach it the wrong lesson. You implement the reward hack, then watch it happen.
Mechanistic Interpretability
residual streams · sparse autoencoders
Reading the internals instead of guessing from the outputs. The closest thing the field has to a debugger, and almost nobody teaches it below PhD level.
AI Safety & Red-Team
OWASP LLM Top 10 · prompt injection CTF
You attack your own system on purpose, in a CTF, before someone else does it by accident in front of your users.
The landscape
Where everything else stops
These are good resources and we send people to all of them. They just each begin somewhere you are expected to have already arrived.
| Starts at | You write the core | Graded | Safety | Access | |
|---|---|---|---|---|---|
| Video coursesKarpathy, 3Blue1Brown | Assumes calculus | Yes, on screen | None | Not covered | Open |
| Library-first coursesfast.ai | Assumes Python | Later, if ever | Notebooks | Not covered | Open |
| Cohort programmesARENA | Assumes deep learning | Yes | By mentors | Core | Application only |
| Certificate platformsCoursera, Udemy | Varies | Rarely | Quizzes | Not covered | Paywalled |
| Obvix LearnThis one | The notation | Every chapter | pytest, in browser | Every chapter | Open, no account |
Why a safety lab wrote a curriculum
Safety work is empirical work
Our research on sycophancy only means something to a reader who can check it — who knows what a residual stream is, why a reward model can be gamed, and how a result gets reproduced. That reader is rare because the path to becoming one barely exists.
So we wrote the path and left it open. Safety is not a final module here; every chapter ends on how that technique fails in the real world — leakage, distribution shift, prompt injection.
Read what we publish“Unreproducible claims are unfalsifiable claims.”
Start at
chapter 00
Go to learn.obvix.io
Five minutes · One lab · No account