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Translating by MjSeven
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Can we build a social network that serves users rather than advertisers?
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======
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translated by hopefully2333
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5 trending open source machine learning JavaScript frameworks
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======
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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][1], 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.)
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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.
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Here are the five trending open source machine learning frameworks in JavaScript.
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### 1\. TensorFlow.js
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[TensorFlow.js][2] 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.
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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.
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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.
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The [machine learning tools][3] 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.
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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.
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### 3\. Keras.js
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[Keras.js][4] 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).
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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).
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### 4\. Brain.js
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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][5] becomes important. It is an open source, JavaScript-powered framework that simplifies the process of defining, training, and running neural networks.
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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.
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### 5\. STDLib
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[STDLib][6] 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.
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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.
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### Conclusion
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If you are a JavaScript developer who intends to delve into the exciting world of [machine learning][7] or a machine learning expert who intends to start using JavaScript, the above open source frameworks will intrigue you.
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Do you know of another open source library that offers in-browser machine learning capabilities? Please let us know in the comment section below.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/5/machine-learning-javascript-frameworks
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作者：[Dr.Michael J.Garbade][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://opensource.com/users/drmjg
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[1]:https://www.liveedu.tv/andreybu/REaxr-machine-learning-model-python-sklearn-kera/
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[2]:https://js.tensorflow.org/
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[3]:https://github.com/mljs/ml
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[4]:https://transcranial.github.io/keras-js/#/
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[5]:https://github.com/BrainJS/brain.js
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[6]:https://stdlib.io/
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[7]:https://www.liveedu.tv/guides/artificial-intelligence/machine-learning/
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五个最热门的开源机器学习 JavaScript 框架
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======
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开源工具的可用性使得开发者能够更加轻松地开发应用，这一点使机器学习领域本身获得了巨大的极高。（例如，AndreyBu，他来自德国，在机器学习领域拥有五年以上的经验，他一直在使用各种各样的开源框架来创造富有魅力的机器学习项目。
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虽然 python 支持绝大多数的机器学习框架，但是 JavaScript 也并没有被抛弃。JavaScript 开发者可以在浏览器中使用各种框架来训练和部署机器学习模型。
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下面是 JavaScript 中最热门五个机器学习框架
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### 1\. TensorFlow.js
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[TensorFlow.js][2] 是一个开源库，它使你能在浏览器中完整地运行机器学习程序，它是 Deeplearn.js 的继承者，Deeplearn.js 不再被提供更新。TensorFlow.js 在 Deeplearn.js 功能的基础上进行了改善，使你能够充分利用浏览器，得到更加深入的机器学习经验。
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通过这个开源库，你可以在浏览器中使用有各种功能的、直观的 API 来定义、训练和部署模型。除此之外，它能够自动为 WebGL 和 Node.js 提供支持。
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如果您有了一个已经训练过的模型，你想要导入到浏览器中。TensorFlow.js 可以让你做到这一点，你也可以在不离开浏览器的情况下重新训练已有的模型。
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现在有很多在浏览器中提供广泛的机器学习功能的资源型开源工具，这个机器学习工具库就是这些开源工具的集合。这个工具库为好几种机器学习算法提供支持，包括非监督式学习、监督式学习、数据处理、人工神经网络（ANN）、数学和回归。
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如果你以前使用 python，现在想找类似于 Scikit-learn 的，能在浏览器中使用 JavaScript 进行机器学习的工具，这套工具会满足你的要求。
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### 3\. Keras.js
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[Keras.js][4] 是另外一个热门的开源框架，它使你能够在浏览器中运行机器学习模型，它使用 WebGL 来提供 GPU 模式的支持。如果你有使用 Node.js 的模型，你就只能在 GPU 模式下运行它。Keras.js 还为使用任意后端框架的模型训练提供支持，例如 Microsoft Cognitive Toolkit (CNTK) 。
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一些 Keras 模型可以部署在客户端浏览器上，包括 Inception v3 (训练在 ImageNet 上)，50 层冗余网络(训练在 ImageNet 上)，和卷积变化自动编码器(训练在 MNIST 上)。
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### 4\. Brain.js
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机器学习里的概念非常重要，它可能会使刚开始进入这个领域的人们气馁，这个领域里的学术用语和专业词汇可能会使初学者感到崩溃，而解决以上问题的能力就是 Brain.js 的优势所在。它是开源的，基于 JavaScript 的框架，简化了定义、训练和运行神经网络的流程。
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如果你是一个 JavaScript 开发者，并且在机器学习领域是完全的新手，Brain.js 能减低你学习的难度曲线。它可以和 Node.js 一起使用，或者运行在客户端浏览器里来训练机器学习模型。Brain.js 支持部分类型的神经网络，包括前馈式网络、Ellman 网络，和门循环单元网络。
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### 5\. STDLib
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[STDLib][6] 是一个基于 JavaScript 和 Node.js 应用的开源库，如果您正在寻找一种在浏览器中运行，支持科学和数字化的基于 web 的机器学习应用，STDLib 能满足你的需要。
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这个库能提供全面而先进的数学和统计学上的功能，来帮助你构建高性能的机器学习模型。你同样也可以使用它扩展的公用程序来构建应用程序和其他的库。除此之外，如果你想要一个数据可视化和探索性数据分析的框架。
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STDLib，你，值得拥有。
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### Conclusion
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如果你是一个 JavaScript 开发者，并且打算深入研究令人兴奋的机器学习世界，或者说，你是一个机器学习方面的专家，打算开始尝试使用 JavaScript ，那么上述的开源框架会激起您的兴趣。
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你有知道其他的，提供在浏览器里运行机器学习功能的开源库吗？请在下面的评论区里告诉我们。
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/5/machine-learning-javascript-frameworks
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作者：[Dr.Michael J.Garbade][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[hopefully2333](https://github.com/hopefully2333)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://opensource.com/users/drmjg
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[1]:https://www.liveedu.tv/andreybu/REaxr-machine-learning-model-python-sklearn-kera/
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[2]:https://js.tensorflow.org/
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[3]:https://github.com/mljs/ml
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[4]:https://transcranial.github.io/keras-js/#/
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[5]:https://github.com/BrainJS/brain.js
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[6]:https://stdlib.io/
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[7]:https://www.liveedu.tv/guides/artificial-intelligence/machine-learning/
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