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今日推薦開源項目：《微信小程序匯總 weixin-xiaochengxu666-info》GitHub鏈接

推薦理由：這是一個微信小程序開源項目庫的集合，裡面包括了UI組件，開發框架，實用庫，開發工具，服務端，還有一些實例和Demo，看完之後才發現微信其實挺厲害的，對於正在進行微信小程序開發的朋友來說無疑值得一看，無論是找到些好用的工具或者是看一看其他的項目相互學習，相信都能有一些收穫。

今日推薦英文原文：《5 trending open source machine learning JavaScript frameworks》作者：Dr. Michael J. Garbade

原文鏈接：https://opensource.com/article/18/5/machine-learning-javascript-frameworks

推薦理由：這篇文章介紹了5個關於機器學習模型的 JavaScript 框架，對於熟悉 JavaScript 的朋友們來說，使用這些框架可能不失為是一個代替 Python 的好辦法

5 trending open source machine learning JavaScript frameworks

The tremendous growth of the machine learning field has been driven by the availability of open source tools that allow developers to build applications easily. (For example, AndreyBu, who is from Germany and has more than five years of experience in machine learning, has been utilizing various open source frameworks to build captivating machine learning projects.)

Although the Python programming language powers most of the machine learning frameworks, JavaScript hasn』t been left behind. JavaScript developers have been using various frameworks for training and deploying machine learning models in the browser.

Here are the five trending open source machine learning frameworks in JavaScript.

1. TensorFlow.js

TensorFlow.js is an open source library that allows you to run machine learning programs completely in the browser. It is the successor of Deeplearn.js, which is no longer supported. TensorFlow.js improves on the functionalities of Deeplearn.js and empowers you to make the most of the browser for a deeper machine learning experience.

With the library, you can use versatile and intuitive APIs to define, train, and deploy models from scratch right in the browser. Furthermore, it automatically offers support for WebGL and Node.js.

If you have pre-existing trained models you want to import to the browser, TensorFlow.js will allow you do that. You can also retrain existing models without leaving the browser.

2. Machine learning tools

The machine learning tools library is a compilation of resourceful open source tools for supporting widespread machine learning functionalities in the browser. The tools provide support for several machine learning algorithms, including unsupervised learning, supervised learning, data processing, artificial neural networks (ANN), math, and regression.

If you are coming from a Python background and looking for something similar to Scikit-learn for JavaScript in-browser machine learning, this suite of tools could have you covered.

3. Keras.js

Keras.js is another trending open source framework that allows you to run machine learning models in the browser. It offers GPU mode support using WebGL. If you have models in Node.js, you』ll run them only in CPU mode. Keras.js also offers support for models trained using any backend framework, such as the Microsoft Cognitive Toolkit (CNTK).

Some of the Keras models that can be deployed on the client-side browser include Inception v3 (trained on ImageNet), 50-layer Residual Network (trained on ImageNet), and Convolutional variational auto-encoder (trained on MNIST).

4. Brain.js

Machine learning concepts are very math-heavy, which may discourage people from starting. The technicalities and jargons in this field may make beginners freak out. This is where Brain.js becomes important. It is an open source, JavaScript-powered framework that simplifies the process of defining, training, and running neural networks.

If you are a JavaScript developer who is completely new to machine learning, Brain.js could reduce your learning curve. It can be used with Node.js or in the client-side browser for training machine learning models. Some of the networks that Brain.js supports include feed-forward networks, Ellman networks, and Gated Recurrent Units networks.

5. STDLib

STDLib is an open source library for powering JavaScript and Node.js applications. If you are looking for a library that emphasizes in-browser support for scientific and numerical web-based machine learning applications, STDLib could suit your needs.

The library comes with comprehensive and advanced mathematical and statistical functions to assist you in building high-performing machine learning models. You can also use its expansive utilities for building applications and other libraries. Furthermore, if you want a framework for data visualization and exploratory data analysis, you』ll find STDLib worthwhile.

Conclusion

If you are a JavaScript developer who intends to delve into the exciting world of machine learning or a machine learning expert who intends to start using JavaScript, the above open source frameworks will intrigue you.

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2018年5月30日：開源日報第83期