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tensorflow/playground

★ 12,900TypeScriptAudience · generalComplexity · 1/5Setup · easy

TLDR

A browser-based interactive tool for experimenting with neural networks visually in real time, letting you adjust layers and settings to see how AI learns without writing any code.

Mindmap

mindmap
  root((TF Playground))
    What it does
      Visualize neural nets
      Real-time training
      No code needed
    Controls
      Layer count
      Nodes per layer
      Dataset choice
    Tech Stack
      TypeScript
      d3.js
      npm build
    Audience
      Students
      Beginners
      Curious learners
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Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Experiment with adding more hidden layers to a neural network and watch how it learns to separate data.

USE CASE 2

Use as a teaching aid to show students how node counts and layer depth change a network's behavior.

USE CASE 3

Run locally to customize the visualization for a course, presentation, or blog post.

Tech stack

TypeScriptd3.jsnpm

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

TensorFlow Playground is an interactive, browser-based tool that lets you experiment with neural networks visually. A neural network is a type of AI system loosely inspired by how the brain works: it takes in data, passes it through layers of connected nodes, and produces a result. This tool shows you what is happening inside that process in real time, without requiring any coding. You can adjust settings like the number of layers, the number of nodes per layer, and what kind of data the network is trying to classify, then watch how the network learns and changes as it trains. It is aimed at people who want to build intuition about how these systems behave, such as students, beginners, or anyone curious about AI. The project is a web application built with TypeScript and a charting library called d3.js. The readme is brief and focused on how to set up a local development environment: install dependencies with npm, build the app, and serve it locally. There is not much additional documentation about what the tool can do beyond what the live demo shows.

Copy-paste prompts

Prompt 1
How do I add a new dataset option to TensorFlow Playground's TypeScript source so students can classify custom shapes?
Prompt 2
Show me how to modify TensorFlow Playground to add a new activation function to the dropdown menu.
Prompt 3
How does d3.js render the live network visualization in TensorFlow Playground? Walk me through the key files.
Prompt 4
I want to embed a TensorFlow Playground-style training animation in my own React app. Which parts of the code handle the live update loop?
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