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This article will explain in detail what is the JavaScript framework for open source machine learning. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

1 、 TensorFlow.js

TensorFlow.js is an open source library that allows you to run machine learning programs completely in your browser. It is the successor to Deeplearn.js and Deeplearn.js is no longer updated. TensorFlow.js has made improvements based on the Deeplearn.js function, so that you can make full use of the browser to get a more in-depth machine learning experience.

With this open source library, you can define, train, and deploy models in browsers using a versatile, intuitive API. In addition, it automatically provides support for WebGL and Node.js.

If you have a trained model, you want to import it into the browser. TensorFlow.js allows you to do this, and you can retrain existing models without leaving the browser.

2. Machine learning tool library

There are many resource-based open source tools that provide a wide range of machine learning functions in browsers, and this machine learning tool library is a collection of these open source tools. This tool library supports several machine learning algorithms, including unsupervised learning, supervised learning, data processing, artificial neural networks (ANN), mathematics, and regression.

If you used to use Python, now you are looking for a Scikit-learn-like tool that can use JavaScript for machine learning in your browser. This set of tools will meet your requirements.

3 、 Keras.js

Keras.js is another popular open source framework that allows you to run machine learning models in browsers. It uses WebGL to support the GPU pattern. If you have a model that uses Node.js, you can only run it in GPU mode. Keras.js also provides support for model training using any back-end framework, such as Microsoft Cognitive Toolkit (CNTK).

Some Keras models can be deployed on client browsers, including Inception v3 (trained on ImageNet), layer 50 redundant networks (trained on ImageNet), and automatic encoders for convolution changes (trained on MNIST).

4 、 Brain.js

The concept in machine learning is very important, it may discourage people who are new to the field, the academic language and professional vocabulary in this field may make beginners feel broken, and the ability to solve the above problems is the advantage of Brain.js. It is an open source, JavaScript-based framework that simplifies the process of defining, training, and running neural networks.

If you are a JavaScript developer and are completely new to machine learning, Brain.js can reduce your learning difficulty curve. It can be used with Node.js or run in client browsers to train machine learning models. Brain.js supports some types of neural networks, including feedforward networks, Ellman networks, and gate loop unit networks.

5 、 STDLib

STDLib is an open source library based on JavaScript and Node.js applications. If you are looking for a scientific and digital web-based machine learning application that runs in a browser, STDLib can meet your needs.

This library provides comprehensive and advanced mathematical and statistical functions to help you build high-performance machine learning models. You can also use its rich features to build applications and other libraries. In addition, if you want a framework for data visualization and exploratory data analysis-STDLib, you deserve it.

This is the end of this article on "what is the JavaScript framework for open source machine learning?". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it out for more people to see.

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