One of the most obvious benefits of using web browsers is that browsers don’t require installation or intricate setup to use the easy access web applications across the internet. A notable prediction for the future app usage is that desktop and phone applications will become obsolete. Desktop and phone apps’ obsolescence is predicted due to the fact that mobile and desktop apps are more difficult and inefficient to use (setup, download, etc) compared to web apps. Ease of use and convenience are two of the most important features that digital consumers seeks in products today and as it stands currently, desktop and phone apps don’t meet those needs.
Web apps are also becoming more sophisticated As such, it’s important that developments in machine learning parallel advancements in web app technology development. There are millions of web developers throughout the world that may have intriguing use for machine learning applications but might not have the training or know-how in how to combine the two fields.
Before diving into how machine learning is used effectively in web browser development, let’s take a look at the elements of machine learning that most affect this relationship. The computational power required by machine learning derives from the use of neural networks and their need to compute/calculate complex matrix arithmetics in huge quantities and sizes.
- opencv4nodejs – Asynchronous Node.js bindings with actual OpenCV
- OpenCV.js for those that know OpenCV, this is a pretty amazing amazing toolas it is literally OpenCV in the browser with all common functionalities.
- NaturalNode – Natural Language Processing in Node.js
- Neataptic – a neural network library for Node.js
- TensorFire – see below for more details
- Decision Trees (Node.js Implementation of Decision Tree using ID3 Algorithm )
- SVM.js -lightweight implementation of the SMO algorithm to train a binary Support Vector Machine
- Brain.js (Neural network library for Node.js)
- face-recognition.js – simple node.js API for robust face detection and face recognition.
- Synaptic (Neural network library)
- WebDNN – quotes on Github as”The Fastest DNN Running Framework on Web Browser”
- deepLearn.js – a hardware-accelerated machine intelligence library for the web
All in all, it is very exciting to see more and more web developers use machine learning directly in web browser development as well as to see web browsers handle such complex implementations faster and more effectively. As machine learning is trending to be a significant part of our technological future, both in enterprise and for consumers, these libraries make for a very optimistic outlook for the future of web applications. Conferences, meetup groups and research developments highlight the significance of this outlook as more varied teams of researchers and developers work together to expand the boundaries of data science’s boundaries.