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How neural networks learn
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Start with a question
Ask what you want to understand. Follow where it leads.
Curiosity rarely arrives fully formed. Ask anyway — the answer will tell you what to ask next.
Wonder
Every answer hides the next question.
Understanding one layer raises new ones. That isn't falling behind — that's curiosity reaching further down.
How it works
A conversation that branches, not a straight line.
Chase one question all the way down. The other ideas aren't buried — each waits on a branch of its own.
01
Circle the word
No new prompt, no re-explaining the background. Selecting it is already the question.
02
Branch a follow-up
The new answer grows under the one that prompted it, so the shape of your thinking is right there on the page.
03
Keep digging
Go down one branch and further down, until nothing is fuzzy left. Want another angle? Start a new branch.
The learning canvas
The whole path, at a glance.
However deep you go, you never lose your way. Collapse answers into summaries and scan the thread at a glance; expand and follow one branch all the way down.
- Summary view: every node folds to a few lines, so the whole tree fits on a screen
- Dig deeper: expand the original answer and select any word to keep asking
- Switch between the two views with one click, and return to any branch
How does memory form?
- ·Neural patterns reactivate and memory consolidates
- ·During sleep the brain replays and strengthens links
3 more, tap to view
What changes at a synapse?
- ·The sending side releases more neurotransmitter
Why does sleep consolidate memory?
- ·Deep sleep replays the day's neural activity
Where does long-term memory live?
- ·Across distributed cortical networks
Canvas summary
How learning changes a network
Training repeatedly nudges a network toward predictions that better match its examples. The change is distributed across many connected weights.
Each training example produces a prediction, measures the gap from the expected result, and turns that gap into a useful correction signal.
The central idea
Backpropagation assigns responsibility for error, while gradient descent determines the size and direction of each update.
From error to improvement
No single weight contains the lesson. Learning emerges from many small updates that gradually reshape how information moves through the network.
Export
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Quiz
Check what actually stuck.
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Review is grounded in the questions and answers you explored, not a generic question bank.
How do neural networks learn?
They improve by comparing a prediction with the expected result, then adjusting the connections that shaped it.
Test this branch
1. What is the main role of backpropagation during training?
A. It stores every training example inside the model.
B. It calculates how much each weight contributed to the error.
C. It selects which examples the model should ignore.
Why this answer is correct
The branch explains that backpropagation carries the prediction error backward to compute gradients for each weight.
Built to keep
Your thinking remains useful after the session.
Keep every conversation
Pick up from the same branch later instead of reconstructing the conversation from memory.
Export clean Markdown
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