A curated collection of standalone examples demonstrating machine learning tasks with TensorFlow.js in browsers and Node.js.
TensorFlow.js Examples is a collection of code samples that demonstrate how to implement various machine learning models and tasks using TensorFlow.js. It solves the problem of learning and applying ML in JavaScript environments by providing concrete, runnable examples that cover everything from loading data and training models to inference and deployment across browsers and Node.js.
JavaScript developers, web developers, and ML practitioners who want to learn or implement machine learning directly in the browser or Node.js using TensorFlow.js.
Developers choose this project because it offers a hands-on, practical way to explore TensorFlow.js capabilities with production-ready, standalone examples that can be easily adapted, reducing the learning curve and accelerating development of ML-powered applications.
Examples built with TensorFlow.js
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Examples span from basic regression to advanced GANs and reinforcement learning, with a detailed README table categorizing each by model type and environment, ensuring broad applicability.
Each example is self-contained and can be directly copied into other projects, reducing integration overhead and accelerating development.
Code runs in browsers, Node.js, Electron, and more, showcasing cross-platform deployment with specific examples like Web Workers and Service Workers.
Includes both high-level Layers API and low-level Core API examples, such as 'mnist-core' vs. 'mnist', catering to different development styles.
The project emphasizes practical code over theoretical explanations, so beginners may struggle with concepts without additional resources, as noted in its philosophy.
The repository is kept small and highly curated per the contributing guidelines, meaning it may not cover niche or emerging ML techniques, leaving gaps for advanced users.
Requires Node.js and build tools like npm/yarn for running examples, adding complexity compared to simpler, standalone scripts or demos.