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2018年6月4日:开源日报第88期2018年6月4日:开源日报第88期

今日推荐开源项目:《方便自定的日程表 tui.calendar》GitHub链接

推荐理由:这是一款基于 JavaScript 的可以方便快速自定义的日程表。这个日程表可以允许你从调整每一天到概览一个月之间转换,当你排了太多日程的时候肯定该试试这个。它最大的特点就是允许你使用鼠标去直接拖动日程,这让调整日程变得极其方便简洁,当然你也可以选择手动更改日程,调整日程的事件类型,地点等等,在更为细致的一周模式和一日模式下你甚至还能写入今天的计划和任务。

按照惯例,tui 系列提供了试用网址:https://nhnent.github.io/tui.calendar/latest/tutorial-example01-basic.html

2018年6月4日:开源日报第88期

今日推荐英文原文:《11 Javascript Machine Learning Libraries To Use In Your App》作者:Jonathan Saring

原文链接:https://blog.bitsrc.io/11-javascript-machine-learning-libraries-to-use-in-your-app-c49772cca46c

推荐理由:继上次提及的五个关于机器学习模型的 JavaScript 框架后,这次要带来的是 JavaScript 中关于机器学习的库,在机器学习这方面上,JavaScript 一样可以是一种选择(brain.js 双双上榜,其实用性可见一斑)

11 Javascript Machine Learning Libraries To Use In Your App

“ Wait, what?? That’s a horrible idea! “

Were the exact words of our leading NLP researcher when I first talked to her about this concept. Maybe she’s right, but it’s also definitely a very interesting concept which is getting more attention in the Javascript community lately.

During the past year our team is building Bit which makes it simpler to build software using components. As part of our work, we develop ML and NLP algorithms to better understand how code is written, organized and used.

While naturally most of this work is done in languages like python, Bit lives in the Javascript ecosystem with its great front and back ends communities.

This interesting intersection led us to explore and experiment with the odd possibilities of using Javascript and Machine Learning together. Sharing from our research, here are some neat libraries which bring Javascript, Machine Learning, DNN and even NLP together. Take a look.

1. Brain.js

Brain.js is a Javascript library for Neural Networks replacing the (now deprecated) “brain” library, which can be used with Node.js or in the browser (note computation ) and provides different types of networks for different tasks. Here is a demo of training the network to recognize color contrast.

2018年6月4日:开源日报第88期
Training Brain.js color contrast recognition

2. Synaptic

Synaptic is a Javascript neural network library for node.js and the browser which enables you to train first and even second order neural network architectures. The project includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks, liquid state machines and a trainer capable of training a verity of networks.

2018年6月4日:开源日报第88期
Training Synaptic image-filter perceptron

3. Neataptic

This library provides fast neuro-evolution & backpropagation for the browser and Node.js, with a few built-in networks including perceptron, LSTM, GRU, Nark and more. Here is a rookie tutorial for simple training.

4. Conventjs

Developed by Stanford U PhD this popular library hasn’t been maintained for the past 4 years, but is definitely one of the most interesting projects on the list. It’s a Javascript implementation of neural networks supporting common modules, classification, regression, an experimental Reinforcement Learning module and is even able to train convolutional networks that process images.

2018年6月4日:开源日报第88期

Conventjs demo for toy 2d classification with 2-layer neural network

5. Webdnn

This Japanese-made library is built to run deep neural network pre-trained model on the browser, and fast. Since executing a DNN on a browser consumes a lot of computational resources, this framework optimizes the DNN model to compress the model data and accelerate execution through JavaScript APIs such as WebAssembly and WebGPU.

6. Deeplearnjs

This popular library allows you to train neural networks in a browser or run pre-trained models in inference mode, and even claims it can be used as NumPy for the web. With an easy-to-pick-up API this library can be used for a verity for useful applications, and is actively maintained.

Deeplearnjs teachable machine web-demo

7. Tensorflow Deep Playground

Deep playground is an interactive visualization of neural networks, written in TypeScript using d3.js. Although this project basically contains a very basic playground for tensorflow, it can be repurposed for different means or used as a very impressive educational feature for different purposes.

2018年6月4日:开源日报第88期
Tensorflow web playground

8. Compromise

This very popular library provides “modest natural-language processing in javascript”. It’s pretty basic and straight forward, and even compiles down to a single small file. For some reason, its modest “good enough” approach makes it a prime candidate for usage in almost any app in need of basic NLP.

2018年6月4日:开源日报第88期
Compromise reminds us of how simple English really is

9. Neuro.js

This beautiful project is a deep learning and reinforcement learning Javascript library framework for the browser. Implementing a full stack neural-network based machine learning framework with extended reinforcement-learning support, some consider this project to be the successor of convnetjs.

2018年6月4日:开源日报第88期
Self-driving cars with Neuro.js

10. mljs

A group of repositories providing Machine Learning tools for Javascript developed by the mljs organization which include supervised and unsupervised learning, artificial neural networks, regression algorithms and supporting libraries for statistics, math etc. Here’s a short walkthrough.

2018年6月4日:开源日报第88期
mljs projects on GitHub

11. Mind

A flexible neural network library for Node.js and the browser, which basically learns to make predictions, using a matrix implementation to process training data and enabling configurable network topology. You can also plug-and-play “minds” which already learned, which can be useful for your apps.

2018年6月4日:开源日报第88期
Really? 0/5? way to predict, mind!

Honorable mentions:

Natural

An actively maintained library for Node.js which provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, and more.

Incubator-mxnet

Apache MXNet is a deep learning framework that allows you to mix symbolic and imperative programming on the fly with a graph optimization layer for performance. MXnet.js brings a deep learning inference API to the browser.

Keras JS

This library runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well.

Deepforge

A development environment for deep learning that enables you to quickly design neural network architectures and machine learning pipelines with built-in version control for experiment reproduction. Worth checking out.

Land Lines

Not even as much of a library as a very cool demo / web game based on a chrome experiment by Google. Although I’m not sure what to do with it, it’s guaranteed to become the most enjoyable 15 minutes of your day.

2018年6月4日:开源日报第88期
Land lines by Google

What’s next?

Obviously, Javascript isn’t becoming the language of choice for Machine Learning , far from it. However, common issues such as performance, Matrix manipulations and abundance of useful libraries are slowly being bridged, closing the gap between common applications and useful Machine Learning.

 


每天推荐一个 GitHub 优质开源项目和一篇精选英文科技或编程文章原文,欢迎关注开源日报。交流QQ群:202790710;微博:https://weibo.com/openingsource;电报群 https://t.me/OpeningSourceOrg