"Translate" 10 machine learning JavaScript examples

Original address: Ten machine learning Examples in JavaScript

In the past year, Libraries for machine learning (machines learning) have become increasingly fast and easy to use. Python has always been the language of choice for machine learning, but now almost all languages are used for neural networks (neural networks), which of course includes javascript!

In recent years, the Web ecosystem has made great strides, and although JavaScript and node. js perform slightly worse than Python and Java, they are enough to handle many machine learning problems. The Web language has a wide and easy-to-use advantage-you can run a machine learning project written in a JavaScript language in just one web browser.

Although the machine learning libraries written in many JavaScript languages are just born and are still being developed, it is worth trying to use them. This article will cover a few JavaScript language-written machine learning libraries and some cool AI Web application examples that can help you get started on AI tours.

1. Brain

Using brain, you can easily create a neural network and train it with input/output data. Because training a neural network consumes more resources, it is recommended to train the neural network in a node. JS environment rather than using a browser directly. On the official website, there is a small demo (PS: recognize color contrast) that can be used to identify colors, and this demo is now 404 pages.

2. Deep Playground

This is an entertaining web app that lets you explore different parts of the neural network in a game-based way. It has a friendly interface for you to control the input of the data, the number of neurons used in the algorithm, and other weight factors that can affect the outcome of the output. This is an open source project, which is a machine learning library written using typescript and has a perfect documentation from which we can do a lot of things.

3. flappylearning

Flappylearning Project about 800 lines of code, this project contains a machine learning library and implements a very interesting demo--learning to play Flappy Bird game. It uses an AI technique called neuroevolution, which uses algorithms generated by the natural nervous system to dynamically learn from every successful or failed iteration.

4. Synaptic. png

Synaptic is a schema-independent (architecture-agnostic), actively maintained node. JS and Browser library that allows developers to build any type of neural network. It has several built-in architectures that allow you to quickly test and compare the similarities and differences between different machine learning algorithms. It also provides documentation on neural networks and several useful demos and other tutorials that will help us learn about machine learning.

5. Land Lines

Land Lines is an interesting Chrome web Experiment (Web experiment) for searching Earth satellite images. This application does not require a service call: It is fully operational in the browser environment and, thanks to the use of machine learning, WEBGL can also perform well on mobile devices. You can browse the source code on GitHub or read the full example here.

6. Convnetjs

Although it is no longer actively maintained, Convnetjs remains one of the most advanced deep learning libraries in JAVASCRIPTP. The library was originally developed by Stanford University, and then Convnetjs began to pop up on GitHub, and the community added many features and tutorials to it. Convnetjs runs directly in the browser environment, supports a variety of learning techniques, and it approaches the underlying principle to make it more suitable for people with experience in neural networks.

7. Thing Translator

Thing Translator is a network experiment that allows your phone to recognize real objects and label objects in different languages. The app is built entirely on web technology and leverages two of the machine learning apis--Google offers for image recognition and the translate API for natural language translation.

8. Neurojs

Nerojs is used to build an AI system framework based on enhanced learning (reinforcement learning). Unfortunately, this open source project does not have complete documentation in addition to the demo of an autonomous driving experiment, and this demo has a good description of the different parts of the neural network. This library is developed using pure JavaScript, such as modern tools such as Webpack and Babel.

9. machine_learning

This is also a library that allows us to create and train neural networks using only JavaScript. It is easy to install into node. js and client environments, and has an API that is friendly to developers. This library provides a number of examples to help you understand the core principles of machine learning.

Ten. Deepforge

Deepforge is an easy-to-use development environment for deep learning. It allows you to create neural networks using simple graphical interfaces, support training models on remote machines, and built-in version control systems. This project is based on node. JS and MongoDB and runs in a browser environment.

Egg: machine learning in Javascript

Burak Kanber released some of the Columns excellent blog post tells the fundamentals of machine learning. These tutorials are well written and designed for JavaScript developers. If you want to understand machine learning in depth, these blog posts are a great resource for learning.

Conclusion

Although JavaScript's machine learning ecosystem is not yet mature, it is recommended to use these resources to open up your machine learning path and build a perceptual understanding of some core technologies. As with some of the experiments shown in this article, you can also create a lot of interesting things using only a browser and a small amount of JavaScript code.

Recommended Reading

Machine learning and AI
TensorFlow

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"Translate" 10 machine learning JavaScript examples