TensorFlow.js supports multiple programming languages for building and deploying machine learning models. The primary languages that are supported by TensorFlow.js are:

  1. JavaScript: TensorFlow.js is mainly designed to work with JavaScript and allows you to use existing JavaScript libraries and frameworks. It provides an API that allows you to create, train, and deploy machine learning models directly in the browser or on Node.js.

  2. TypeScript: TensorFlow.js has full TypeScript support, which provides static typing and improved developer tooling. TypeScript is a superset of JavaScript and adds static typing and other features to help developers write cleaner code and catch errors early.

  However, TensorFlow.js also provides support for other languages through language bindings. Language bindings allow you to access TensorFlow.js from different programming languages. Some of the language bindings available for TensorFlow.js include:

  3. Python: TensorFlow.js provides a Python library called TensorFlow.js Python, which allows you to convert pre-trained TensorFlow models to the TensorFlow.js format. This enables you to leverage the wide range of machine learning models and tools available in Python with TensorFlow.js.

  4. Java: TensorFlow.js provides Java language bindings through TensorFlow.js Java, enabling developers to use TensorFlow.js from Java applications.

  5. Go: TensorFlow.js also supports Go programming language with TensorFlow.js Go bindings, allowing developers to use TensorFlow.js in Go applications.

  These language bindings provide developers with flexibility and allow them to use TensorFlow.js with their preferred programming language and ecosystem. However, it's important to note that the full capabilities of TensorFlow.js may not be available in all languages, and the primary focus is on providing support for JavaScript and TypeScript.