We’ve covered machine learning in our previous article on Hot Topics in Web Development, so make sure you’ll check it for more information on other trends currently rocking the dev world. Herein specifically, we’ll concentrate on machine learning, languages involved, and look at the TOP Best
Machine Learning libraries that are written in
Let’s briefly define Machine Learning before we move on to the fun part. Machine Learning from now on referred to as ML) is a branch of
Artificial Intelligence (AI) that’s based on the idea that systems can learn from data, analyze and establish patterns, and make decisions based on those findings without the need for or with minimal human intervention and interaction. To have a brief overview of the data science, consider reading DS: Bigger Picture on the blog.
Speaking of languages, ML is usually associated with Python and R.; first of all, because these languages are suitable for non-programmers and there are comprehensive ML libraries available, which make them easy to use. In other cases, ML algorithms are implemented in
Scala. However, since during the past couple of years JS popularity skyrocketed, more and more people have started using
JS, creating some awesome JS ML libraries, enabling implementation of ML methods both in the browser and on the backend using
Natural is a general natural language facility for Node.js. It currently supports:
nlp.js is an NLP library built-in node over Natural.
nlp.js is currently able to
There is a version of NLP.js that works in React Native, so you can build chatbots that can be trained and executed in the mobile environment without the need for the Internet connection.
It makes easy to publish networks on Web pages and allows developers to integrate network exploration in rich Web applications. Suitable for both beginners and advanced users alike.
One representative example: https://bl.ocks.org/mbostock/raw/3231298/
Nivo is built on top of the awesome d3 and Reactjs libraries.
Nivo is all about React components built on top of d3 that help build DataViz apps with ease.
brain.js is focused on training and applying feedforward and recurrent neural networks. It also provides such advanced options as using GPU to train networks, asynchronous training that can fit multiple networks in parallel, and cross-validation, a more sophisticated validation method. brain.js saves and loads models to/from JSON files.
A fun and practical 19-part course on Brain.js can be found here.
Ml.js is machine learning and numerical analysis tools for Node.js and the Browser.
Ml.js is a comprehensive general-purpose Machine Learning library written in JS. The library itself is a compilation of the tools developed in the mljs organization. Although it’s primarily written for use in the browser, you may add your own dependencies to use in Node.js as well: those are labeled with ml-, so — pretty easy to find. The library supports the following routines: bit operations on arrays, sorting, hash tables, random number generation; linear algebra, array manipulation, optimization, statistics; cross-validation, supervised and unsupervised learning.
ml.js supports the following unsupervised learning methods: principal component analysis, hierarchical clustering, and K-means clustering.
Among the supervised learning you’ll find the following methods:
If you want to learn more about this library, then this is a great video to start with:
Originally published by Marina Vorontsova at blog.soshace.com
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You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.
Machine Learning is an utilization of Artificial Intelligence (AI) that provides frameworks the capacity to naturally absorb and improve as a matter of fact without being expressly modified. AI centers round the improvement of PC programs which will get to information and use it learn for themselves.The way toward learning starts with perceptions or information, for instance , models, direct understanding, or guidance, so on look for designs in information and choose better choices afterward hooked in to the models that we give. The essential point is to allow the PCs adapt consequently without human intercession or help and modify activities as needs be.