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Neural Networks
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@maya is typing about backpropagation
Neural Networks
A field guide to how machines learn

A neural network is a function that learns from examples. It maps inputs to outputs by adjusting thousands of small weights, one gradient step at a time.

Backpropagation measures how much each weight contributed to the error, then nudges it in the right direction. Do this millions of times and patterns begin to emerge.

Synapse AI

Concepts in this note: neural network, gradient descent, activation function, backpropagation. Would you like to turn this into flashcards?

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Why Synapse

A thinking tool, not just a text box

Real-time, lossless editing

Type beside teammates with live cursors and zero lost work, powered by an Operational Transformation engine we built from scratch.

AI that studies back

Turn any note into summaries, flashcards, and quizzes. The same text that teaches you also teaches itself.

A map of your thinking

Synapse surfaces the concepts hiding in your notes and draws the links between them as a living knowledge graph.

How it works

From blank page to connected knowledge in three steps

01

Create a document

Start a note in the editor, just like a blank page in a notebook. Write with markdown, headings, and rich text from the start.

02

Invite your team

Share a link. Teammates join the same document and edit alongside you, every cursor and keystroke kept in sync.

03

Watch the graph grow

As you write, Synapse extracts the concepts and quietly builds a graph of how your ideas connect.

Knowledge graph

Watch your notes become a network

Every note is a node. As you write, Synapse connects related concepts into a living map of what you're learning, then lets you explore the links between them.

Explore your graph
Operational Transform
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Latency
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