# TensorFlow.js — Machine Learning in JavaScript

> A JavaScript library for training and deploying machine learning models in the browser and on Node.js with GPU acceleration via WebGL and WebGPU.

## Install

Save in your project root:

# TensorFlow.js

## Quick Use
```bash
npm install @tensorflow/tfjs
```
```js
import * as tf from '@tensorflow/tfjs';
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
```

## Introduction
TensorFlow.js brings the TensorFlow ecosystem to JavaScript, letting you train models directly in the browser or on Node.js. It provides GPU-accelerated tensor operations through WebGL and WebGPU, making client-side ML inference fast enough for real-time applications.

## What TensorFlow.js Does
- Trains and runs ML models entirely in the browser with no server required
- Converts existing TensorFlow and Keras models for JavaScript deployment
- Accelerates tensor math via WebGL, WebGPU, and WASM backends
- Provides pre-built models for image classification, object detection, pose estimation, and NLP
- Supports transfer learning to fine-tune models on client-side data

## Architecture Overview
The library has three layers: a low-level Ops API for tensor operations, a Layers API matching the Keras interface, and a high-level Model API for loading pre-trained models. Backends (WebGL, WebGPU, WASM, CPU) are pluggable and selected automatically based on browser capabilities. A converter tool transforms SavedModel and Keras H5 files into the TFJS format.

## Self-Hosting & Configuration
- Install the core package or use a CDN script tag for quick prototyping
- Choose a backend explicitly or let the library auto-select the fastest available
- Use the Node.js binding with tfjs-node for server-side training with native C++ acceleration
- Convert Python models using the tensorflowjs_converter CLI tool
- Host converted model files on any static file server or CDN

## Key Features
- Full training and inference loop runs in the browser with no backend calls
- WebGPU backend delivers near-native GPU performance on supported browsers
- Extensive pre-trained model zoo for vision, language, and audio tasks
- Seamless model conversion from Python TensorFlow and Keras
- Privacy-preserving ML since data never leaves the user device

## Comparison with Similar Tools
- **ONNX Runtime Web** — inference-only for ONNX models; TensorFlow.js supports both training and inference
- **Brain.js** — simpler neural network API; TensorFlow.js offers the full TensorFlow ecosystem
- **PyTorch** — Python-first; TensorFlow.js targets JavaScript runtimes natively
- **ml5.js** — higher-level wrapper built on TensorFlow.js for beginners
- **Transformers.js** — focuses on Hugging Face model inference; TensorFlow.js covers broader ML tasks

## FAQ
**Q: Can I train large models in the browser?**
A: Small to medium models train well. For large models, train in Python and convert for browser inference.

**Q: Which backend should I use?**
A: WebGPU gives the best performance on supported browsers. WebGL is the most widely compatible fallback.

**Q: Can I use pre-trained Hugging Face models?**
A: TensorFlow.js supports converted TF/Keras models. For Hugging Face transformers, consider Transformers.js.

**Q: Does it work in Node.js?**
A: Yes. The tfjs-node package provides native C++ bindings for server-side performance.

## Sources
- https://github.com/tensorflow/tfjs
- https://www.tensorflow.org/js

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Source: https://tokrepo.com/en/workflows/asset-584537ac
Author: AI Open Source