There are several alternative frameworks to TensorFlow.js that you can consider depending on your specific needs. Some of the popular ones are:

  1. Keras.js: Keras.js is a JavaScript implementation of the popular Keras deep learning library. It provides a high-level API for building and training neural networks and can run on both CPU and GPU. Keras.js supports multiple backends, including TensorFlow.js.

  2. Deeplearn.js: Deeplearn.js is an open-source library developed by Google. It provides low-level building blocks for implementing deep learning models in JavaScript. Deeplearn.js allows you to efficiently perform computations on GPUs using WebGL.

  3. Brain.js: Brain.js is a lightweight JavaScript library that focuses on providing neural networks for browser-based applications. It provides a simple API for creating and training neural networks. However, Brain.js may not have all the advanced features that TensorFlow.js offers.

  4. Ml5.js: Ml5.js is a library built on top of TensorFlow.js that simplifies the use of machine learning models in the browser. It provides a high-level API for common tasks such as image classification, object detection, and style transfer. Ml5.js also supports pre-trained models that can be easily integrated into your web applications.

  5. Synaptic.js: Synaptic.js is a flexible and lightweight JavaScript library for building neural networks. It allows you to create and train various types of neural networks, including feedforward, recurrent, and probabilistic neural networks. Synaptic.js provides an intuitive API for defining network architecture and conducting training.

  These are just a few examples of alternative frameworks to TensorFlow.js. Each framework has its own strengths and weaknesses, so it's important to evaluate them based on your specific requirements and project constraints.