TensorFlow.js is an open-source JavaScript library developed by Google that enables machine learning (ML) models to be defined, trained, and run directly in the browser or in Node.js. This allows developers to leverage the power of machine learning in JavaScript environments, making it possible to build and deploy ML models in web applications, on the server side, or even in mobile hybrid applications.

### Key Features of TensorFlow.js:
1. **In-Browser Machine Learning**: TensorFlow.js allows for training and deploying ML models entirely in the browser. This makes it possible to leverage the user's hardware (like the GPU) for accelerated computations, reducing the need for server resources and enabling real-time interactive applications.

2. **Node.js Support**: TensorFlow.js can also run in Node.js, which allows for server-side machine learning or running ML tasks in environments where Python might not be available or preferred.

3. **Pre-trained Models**: TensorFlow.js offers a variety of pre-trained models that can be easily used out-of-the-box for tasks such as image classification, object detection, and text processing.

4. **Model Conversion**: It provides tools to convert TensorFlow or Keras models (originally written in Python) to a format that can be run in JavaScript. This means that models trained in Python can be deployed in web or Node.js environments.

5. **Custom Models**: Developers can build, train, and fine-tune custom machine learning models directly in JavaScript, leveraging the full capabilities of the TensorFlow.js library.

6. **Hardware Acceleration**: TensorFlow.js automatically uses WebGL to accelerate computations in the browser, enabling better performance on compatible devices.

### Use Cases:
- **Web-based ML applications**: Such as image recognition, sentiment analysis, or real-time object detection within web pages.
- **Server-side ML**: Running or training models in a Node.js environment.
- **Hybrid Mobile Apps**: Integrating machine learning models into mobile applications that use web technologies.

TensorFlow.js opens up opportunities to integrate machine learning into a wide range of applications where Python might not be ideal, especially in environments that prioritize JavaScript.