Retrieval
Try explaining it yourself.
Learn through an example, then try a focused recall question. Already know the topic? Start with a cold check.
Recall it coldOne nudgeExplain the gap
The space between knowing & understanding
Understand the ML you work with.
For engineers learning a concept, closing a gap, or preparing for an interview. Explore a worked example, follow the math and code, and practise explaining it yourself.
2 free sessions, no account needed.
2full sessions$0no account157guided topics
Study helps you understand ML. Studio helps you explain it.
Change the learning rate and follow the path.Study a concept
Try the learning loop
See what changes when an idea clicks.
Explore a worked correction, then answer a related question without the notes. Or open the example session to follow the full explanation.
Learn a concept →
Start with a worked example. Basic programming is enough; explore unfamiliar math as you go.
Prepare for an interview →
Practise explaining mechanisms and defending implementation tradeoffs.
Check what you know →
Already comfortable with ML? Diagnose advanced training and systems failures.
Reinforcement learning · delayed reward
Why take a step with no reward?
A worked Q-learning example. Explore the correction, then try a different transition.
An agent moves toward a goal. The move earns zero reward, but the episode continues.
“No reward means this action was worthless. Its value should move toward zero.”
That misses what the next state makes possible. Q-learning considers estimated future rewards too.
Explore the full Q-learning session: intuition, math, and code
Q-learning: from delayed reward to an update.
An example session with math, code, and follow-up questions.
A route you can explore at your own pace
From an example to your own explanation.
Start with the idea, inspect how it works, then try using it. Ask a follow-up or request a deeper explanation at any point.
Orient
See the whole idea before the machinery.
Start with the problem, the constraints, and the mental model that makes every later equation feel inevitable.
concept
problemconstraintuse caseBuilt for durable recall
A record of what holds up.
A chat remembers the thread. LiminalML remembers what you could retrieve, which implementation checks passed, and what is likely to fade next.
Implementation
def attention(q, k, v, mask):
scores = q @ k.transpose(-2, -1)
scores /= q.size(-1) ** 0.5
scores.masked_fill_(~mask, float("-inf"))
return scores.softmax(-1) @ v Example checks: output shape / finite gradients
Retention
Come back when recall is at risk.
The queue uses recorded gaps, elapsed time, and prerequisites to choose a small number of worthwhile reviews.
Attention / mathdue today
Position / intuitionholding
Evidence
Not one vague mastery score.
Keep separate evidence for intuition, math, implementation, systems decisions, failure modes, hints, and tests.
illustrative evidence record
Systems: strong · Math: due · Implementation: 6/6 checks · Hint use: none
Open technical references
17 authored references, open to everyone.
Deep LearningAttention MechanismsTurn compatibility scores into attention weights, then combine values. Explore the geometry, normalization, and limits of the mechanism.9 min · deep diveDeep LearningTransformer ArchitectureFollow attention, feed-forward layers, normalization, and residual connections through a Transformer block.9 min · deep diveDeep LearningBackpropagationTrace local derivatives backward through a computation and connect the chain rule to a manual implementation.9 min · deep diveLLMsKV CacheSee which token projections caching saves, what attention must still read, and how memory use grows with context.8 min · deep dive
Two tracks, one method
Choose the depth. Keep the structure.
157 topics for AI-guided sessions, with 17 separately authored ML references you can read without an account. You choose the topic and pace; the tutor can explain prerequisites.
ML / Research71 topics
From classical models to training systems.
Transformers, optimization, RLHF, distributed training, serving, and the mathematics underneath modern systems.
Software engineering86 topics
From browser internals to distributed systems.
Rendering, caching, databases, data structures, backend architecture, and production tradeoffs.
Pricing
Start learning. No account needed.
Only Pro takes a card. The trial does not charge for seven days. Cancel before it ends to avoid a charge.
Guestno account
$0
2 sessions total in this browser
Two complete six-stage sessions
157 topics for guided AI sessions
17 authored technical references to read
No email, no card, no profile
Sign in to save sessions and use the practice lab
Freesign in
$0
8 sessions per calendar month
8 sessions a month, no card
Saved threads you can resume
Mastery evidence and an adaptive review queue
12 retrieval evaluations a month
5 practice-lab code reviews a month
Revision cards
Optional resume context for worked examples
Pro7-day trial
$9 / month
Unlimited sessions
Unlimited review sessions
Unlimited practice-lab code reviews
Unlimited retrieval evaluations and mastery history
Everything in Free
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A session is one topic explored across six stages. Guest sessions are a one-time allowance; Free session, retrieval, and code-review allowances reset each calendar month. Reading the authored references does not use a session.
Start before you sign up
Pick the concept you keep almost understanding.
Work through the details, ask the follow-up, and test what stayed with you.
Start a deep study session2 sessions without an account · 8 / month when you sign in · unlimited on Pro