[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70675":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},70675,"playground","tensorflow\u002Fplayground","tensorflow","Play with neural networks!","http:\u002F\u002Fplayground.tensorflow.org",null,"TypeScript",12930,2730,464,99,0,5,32,15,45,"Apache License 2.0",false,"master",true,[],"2026-06-12 02:02:41","# Deep playground\n\nDeep playground is an interactive visualization of neural networks, written in\nTypeScript using d3.js. We use GitHub issues for tracking new requests and bugs.\nYour feedback is highly appreciated!\n\n**If you'd like to contribute, be sure to review the [contribution guidelines](CONTRIBUTING.md).**\n\n## Development\n\nTo run the visualization locally, run:\n- `npm i` to install dependencies\n- `npm run build` to compile the app and place it in the `dist\u002F` directory\n- `npm run serve` to serve from the `dist\u002F` directory and open a page on your browser.\n\nFor a fast edit-refresh cycle when developing run `npm run serve-watch`.\nThis will start an http server and automatically re-compile the TypeScript,\nHTML and CSS files whenever they change.\n\n## For owners\nTo push to production: `git subtree push --prefix dist origin gh-pages`.\n\nThis is not an official Google product.\n","tensorflow\u002Fplayground 是一个用于神经网络交互式可视化的项目。它使用 TypeScript 和 d3.js 开发，提供了一个直观的界面来探索和理解神经网络的工作原理。用户可以通过调整参数、选择不同的激活函数和损失函数等，实时观察神经网络在不同数据集上的训练过程和效果。该项目非常适合教育场景，帮助初学者学习和理解深度学习的基本概念，同时也适合研究人员和开发者快速测试和验证神经网络模型的设计思路。",2,"2026-06-11 03:33:33","high_star"]