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10 JavaScript Machine Learning Frameworks Kids Must Know

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The popularity of JavaScript doesn’t need any special introduction. And moreover, businesses are found adopting machine learning and artificial intelligence in their routine operations. To date people used to apply machine learning (ML) methods and algorithms using either of the two programming languages; i.e. Python or R according to Github. Now the trend is shifting gradually towards the upcoming best programming language which is JavaScript.

What is a Framework?

A framework is a platform for developing software applications. It provides a foundation on which software developers can build programs for a specific platform. For example, a framework may include predefined classes and functions that can be used to process input, manage hardware devices, and interact with system software. This helps in streamlining the development process since programmers don’t need to rewrite the code each time they develop a new application.

Important Terms

Before moving on to the various JavaScript frameworks, let’s take a look at some of the related terms.

Feedforward Neural Network: A Feedforward Neural Network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendent recurrent neural networks.

The Feedforward neural network was the first and simplest type of neural network devised. In this network, the information moves in only one direction – forward – from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.

Long Short-Term Memory (LSTM): Long Short-Term Memory (LSTM) network is an artificial recurrent neural network (RNN) architecture used in the field of Deep Learning. Unlike standard Feedforward neural networks, LSTM has feedback connections. It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (Intrusion Detection Systems).

A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell.

LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown durations between important events in a time series. LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional RNNs. Relative insensitivity to gap length is an advantage of LSTM over RNNs

Recurrent Neural Network (RNN): A Recurrent Neural Network is a class of artificial neural networks where connections between nodes form a directed graph along  a temporal sequence. This allows it to exhibit temporal dynamic behaviour. Derived from Feedforward Neural Networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition, or speech recognition.

The term “Recurrent Neural Network” is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. Both classes of networks exhibit temporal dynamic behaviour. A finite recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite recurrent network is a directed cyclic graph that cannot be unrolled.

Both finite impulse and infinite impulse recurrent networks can have traditional stored states, and the storage can be under direct control by the neural network. The storage can also be replaced by another network or graph, if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated state or gated memory, and are part of Long Short-Term Memory Networks (LSTMs) and gated recurrent units. This is also called Feedback Neural Network (FNN).

ML Frameworks in JavaScript

JavaScript is the most popular cross-platform language with a mature Node Package Manager (npm) among developers. Npm is an online repository for the publishing of open-source Node.js projects and it is a command-line utility for interacting with said repository that aids in package installation, version management, and dependency management. A plethora of Node.js frameworks and applications are published on npm, and many more are added everyday. These applications can be searched for on https://www.npmjs.com/. Once you have a package you want to install, it can be installed with a single command-line command.

If you are a beginner or a JavaScript developer and want to float in a Machine Learning pool then some free JavaScript framework might prove to be fruitful for you. Machine learning is taking shape with an exponential rate, It is making the process of developing the application very easy. It can’t be denied that Python has retained a special place in Machine Learning, but still, JavaScript can’t be left behind.

Here are the 10 best JavaScript frameworks for kids to try their hands.

1. Brain.js

Brain.js is a GPU accelerated framework of neural networks written in JavaScript for browsers and Node.js. It is simple, fast and easy to use. It provides multiple neural network implementations as different neural nets can be trained to do different things well.

It is simple to use and performs computations using GPU and fallback to pure JavaScript when GPU is unavailable. Brain.js provides multiple neural network implementations as different neural networks can be trained to do different things well.

ML Frameworks in JavaScript

The framework greatly simplifies building and training Neural Networks to just a few lines of code eliminating much of the math and jargon needed to fully understand the theoretical aspect of the model.

Brain.js supports a few different Neural Network types like Feedforward (ANN) and LSTM (Long Short Term Memory) network.

2. ConvNetJS

ConvNetJS is a JavaScript framework for training Deep Learning models (neural networks). The library allows a user to formulate and solve neural networks in JavaScript while supporting common neural network modules. It also has the ability to specify and train Convolutional Networks that process images, experimental Reinforcement Learning modules and more.

It is well suited for creating and training your network. You can build a network by specifying layers of various types. As one of the types is convolutional, you can build networks that recognize images. However, convolutional image recognizers aren’t the only possibility and you can create general classifiers, regression prediction networks, and more.

ML Frameworks in JavaScript

Once you have the network definition you can train it using backprop or to minimize a sum of squared errors to learn arbitrary data in regression applications. 

There is also a MagicNet training class that handles the training automatically for you. If you want to be cutting edge then you could even try out the Deep Q reinforcement learning class to see if you learn to play games given only the outcome.

If you just want to see neural networks in action there are nine demos that you can run in your browser. They are also very well presented. You get a graph of the error (Loss) as the network trains and you can change the usual learning parameters dynamically. Scrolling down reveals a section that provides insights into how the network is doing the job. You can see the features being used to distinguish between the examples. Finally you get a sample of the network’s performance based on what it does to a number of test series.

3. Compromise

Compromise is a JavaScript framework that interprets and pre-parses text. It is a rule-based Natural Language Processing (NLP) framework that prefers the smallest, least-fancy solutions to getting a text into a manageable form.

It is a framework aiming to be a compromise between speed and accuracy. The aim is to have a client-side parsing library so fast that it can run as you’re typing while still providing relevant results.

4. Synaptic

Synaptic is a JavaScript Neural Network framework for node.js and the browser. This framework includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTMs), liquid state machines or Hopfield networks, and a trainer capable of training any given network. The generalized architecture of this framework is architecture-free so that a user can build and train any first-order or even second-order neural network architectures.

5. ml5.js

ml5.js is an open-source Machine Learning framework written in JavaScript. The framework is a friendly high-level interface to TensorFlow.js and can handle GPU accelerated mathematical operations, along with memory management for Machine Learning algorithms. 

ML Frameworks in JavaScript

The ml5.js provides immediate access in the browser to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships, and much more.

6. Stdlib-js

Stdlib-js is a standard library for JavaScript and Node.js. With an emphasis on numerical and scientific computing applications, this framework provides a collection of robust, high-performance libraries for mathematics, statistics, data processing, streams, and much more. The features of this framework include 150+ special math functions, 35+ probability distributions, 40+ seedable pseudorandom number generators and other such.

7. Mind

Written in JavaScript, Mind is a flexible Neural Network framework for Node.js and the browser. Some of the features of Mind are that it is vectorised as it uses matrix implementation to process training data, it allows users to customize the network topology. It is also pluggable, i.e., it allows downloading and uploading minds that have already learned.

8. machinelearn.js

machinelearn.js is a Machine Learning framework for the web and node written in Typescript. The framework solves Machine Learning problems and teaches users how Machine Learning algorithms work. By default, machinelearning.js uses a pure JavaScript version of tfjs.

9. neuro.js

neuro.js is a Machine Learning framework for building AI assistants and chat-bots. It is a library for developing and training Machine Learning models in JavaScript and deploying in the browser or on Node.js. The library supports multi-label classification, online learning, and real-time classification.

ML Frameworks in JavaScript

10. Deeplearnjs

Deeplearn.js is an open-source hardware-accelerated JavaScript library for machine intelligence. The framework brings performant Machine Learning building blocks to the web, allowing a user to train Neural Networks in a browser or run pre-trained models in inference mode. Deeplearn.js has two APIs, an immediate execution model and a deferred execution model mirroring the TensorFlow API.

ML Frameworks in JavaScript

Which of these frameworks will you use in your next ML project?

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