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How to use Nodejs for machine learning?

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Javascript is commonly thought of as a web programming language, although Javascript and Javascript frameworks such as NodeJS have many applications other than online apps. These applications include desktop applications, mobile applications, embedded systems, and back-end development. If you work in web development (as I do) and want to explore new machine learning applications (as I do), you may be wondering, “Do I have to invest time learning a whole new programming language to explore machine learning?” No, not at all. Many different programming languages and frameworks, including NodeJS, can be used to investigate Nodejs for machine learning concepts. In this article, I will go through some of the best Nodejs for machine learning for NodeJS to get you started.

List of use Nodejs for machine learning

  1. ml.js
  2. Brain.js
  3. Synaptic
  4. Limdu.js
  5. ConvNetJS
  6. Stdlib
  7. TensorFlow.js
  8. KerasJS
  9. NeuroJS
  10. Mind
  11. Natural
  12. Compromise

Details are here for use Nodejs for machine learning

1. Ml.js

The ml.js organisation created this library as a set of tools. It contains a large number of libraries classified as unsupervised learning, supervised learning, artificial neural networks, regression, optimization, statistics, data processing, and maths utilities.

Most of the libraries included in ml.js are designed to be used in a web browser; however there is an npm package for working with them in a Node.js environment.

Check out these examples of ml.js utilities:

Classifier based on Naive-Bayes

Each tool utility is accessible as a distinct module, so you don’t have to install them all at once if you don’t want to. You can immediately import your JavaScript project after installing the package. ES6 modules are also supported by ml.js.

Finding the little value in an array

The utilities that aid in the transformation and computation of values utilising arrays are packaged individually and are listed under ml-array, which can be found here. Assume you want to find the minimal value of all the inputs provided as an array. To do so, first install the ml-array-min programme.

2. Brain.js

Brain.js is a JavaScript library for neural networks that can be used with both Node.js and in the browser. You may easily access and use it by installing it with npm. Because it is written in JavaScript, it can train network data asynchronously using trainAsync () and also supports streams.

In general, neural networks are regarded as a math-heavy sub domain of machine learning. Brian.js performs an excellent job of simplifying the process of establishing and training a neural network by using the simplicity of JavaScript and reducing the API to only a few method calls and variables.

Brain.js is utilised in a several of useful machine learning app. Consider Cracking Captcha with Neural Networks, in which the author utilizes captcha photos as the dataset and uses image processing and the Brain.js module to develop a neural network that recognizes each individual character.

3. Synaptic

Synaptic is another Node.js JavaScript neural network. It includes architectures such as multilayer perceptrons; multilayer long-short term memory networks (LSTM), liquid state machines, or Hopfield networks, as well as a trainer capable of training on different networks. It is also compatible with your browser.

See the example below for guessing the next character in a stream of text-based Wikipedia articles using a long-short term memory.

Synaptic and Brain.js are both written in JavaScript. Synaptic will almost certainly have similar use cases to the prior one.

I’ll give you another reason to prefer synaptic versus Brain.js. It includes more API features than the previous library discussed in this article. This implies that Synaptic will have additional application cases and will be actively developed.

Consider the following popular use cases for this machine learning module:

  • XOR Problem Solution
  • Making a painting
  • Using Wikipedia as a source

4. Limdu.js

Limdu.js is a Node.js machine learning framework that supports binary, multi-label, feature engineering, online learning, and real-time classification. It is currently in alpha and seeking contributors.

More applications for this library can be found by following the link below. Here are a few examples:

Serialization: You may train a classifier on your home computer and then deploy it to a remote server. To accomplish this, you must transform the trained classifier to a string, transport the string to the remote server, and then desterilize it there.

Multi-label classification: The output of binary classification is either 0 or 1. The output of multi-label is a set of zero or more labels.

5. ConvNetJS

A Stanford University PhD developed the ConvNetJS JavaScript neural network implementation, which now supports typical neural network modules, SVM, regression, and the ability to train convolution networks to interpret pictures.

It comes with excellent documentation and in-browser demos. These are some examples of how ConvNetJS might be used:

Painting a Picture (Regreesion)

MNIST digits are used to train an auto encoder.

Speed test

6. Stdlib

Stdlib is a JavaScript library for creating complex statistical models and machine learning libraries. It also has charting and graphics capabilities for data visualization and exploratory data analysis. Stdlib provides support in the form of libraries for linear regression, binary classification, and natural language processing.

When it comes to offering tooling assistance, this library is massive. It has the following advantages:

  • It comes with its own REPL environment, as well as integrated help and examples.
  • Native add-ons providing pure JavaScript fallbacks for interacting with BLAS libraries
  • There are around 50 sample datasets for testing and development.
  • There are more than 200 assertion tools for data validation and feature discovery.
  • There are more than 200 general utilities for data transformation, functional programming, and asynchronous control flow.
  • Browserify, Webpack, and other bundlers can be used to bundle for use in web browsers.

7. TensorFlow.js

TensorFlow.js is a free and open-source JavaScript library for training and deploying machine learning models. It is one of the most well-known libraries in the world. To develop models from scratch, you can use the low-level JavaScript linear algebra library or the high-level layers API, which are both flexible and simple to use.

TensorFlow.js has more implementations than any other library discussed in this article. There’s a good explanation behind this. Not only is it regularly developed, but it also allows you to write, train, and deploy GPU-based DL models in JavaScript.

8. KerasJS

In many aspects, it is comparable to Tensorflow.js. One similarity is that KerasJS supports high-level APIs that handle the abstraction offered by backend frameworks. Models may be trained in any backend using KerasJS, and you can even use TensorFlow.js to do so.

9. NeuroJS

NeuroJS is a deep learning JavaScript framework. It is primarily intended for reinforcement learning, but it can be applied to any neural network-based activity. It includes interesting examples to demonstrate these capabilities, such as a 2D self-driving automobile. Support for deep-q-networks and actor-critic models are also included.

10. Mind

This is another Node.js neural network library that uses matrix implementation to process training data. It does allow you to customise the network topology and use plugins created by the community. These plugins typically allow you to configure pre-trained networks that can make predictions right away.

11. Natural

Natural is a tokenizing, stemming, classification, phonetics, tf-idf, WordNet, and string similarity library. In other words, this library provides language facilities that can be used as a Node.js module. This is an intriguing idea with a wide range of applications.

All of these utilities have been bundled together and made available as a package that can be readily installed using npm: npm install -S natural

This library appears to be in its early stages, with most algorithms being English-specific, but recent community-based contributions have incorporated support for other languages such as Russian and Spanish.

12. Compromise

Another example of natural language processing that is only 230kb when utilised in the browser. There is a benefit of utilising this library over the previously described Natural.

This library contains a variety of simple and easy-to-use tools, as well as support for community-created plugins to enhance and use pre-configuration that allows adding vocabulary, resolving mistakes, and defining context rapidly.

Support for languages other than English, such as German and French, is still in the works.

Conclusion

These sophisticated libraries show that JavaScript is not behind in machine learning applications. Several machine learning library developers and teams write libraries in C, LIBSVM, LIBLINEAR, etc. Node.js core API extensions can implement these. This tutorial use Nodejs for machine learning should help you learn and use Node.js libraries. We hope that our viewers like this article, if so please leave your comment in the comment section below.For more information you can visit website https://techdeposits.com/

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