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How neural networks learn
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Quiz · 4 questions
Start with a question
Ask what you want to understand. Follow where it leads.
Your first question is the root. Every follow-up puts out a new branch.
Explore
Let each answer reveal the next question.
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How it works
A conversation that branches, not a straight line.
Every branch stays attached to where it grew from, so you can see why you asked.
01
Select a concept
Choose the exact word or claim you want to understand instead of rebuilding the context in a new prompt.
02
Branch a follow-up
Ask the next question from that point. Ramifly carries the relevant context into the new answer.
03
Keep the whole picture
Return to earlier branches, compare paths, and see how each part contributes to the topic as a whole.
The learning canvas
Context stays where you can see it.
Questions and answers grow into a tree, and you can see where every branch split off.
- Zoom out to recover the big picture
- Return to any branch without losing your place
- See which idea prompted every follow-up
How does memory form?
Memory is strengthened when patterns of neural activity are reactivated and stabilized over time.
「synapse」
What changes at a synapse?
「reactivated」
Why does retrieval strengthen memory?
「stabilized over time」
How does sleep support consolidation?
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.
Summary
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Quiz
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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
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