As a developer, having and using the right JavaScript machine learning Libraries will help you in the quest for putting together an algorithm that will tap into the strengths and capabilities of the machine learning project of your choice. In this article, 10 best open-source JavaScript machine learning libraries to extend JavaScript’s capabilities are discussed.
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Top JavaScript libraries for Machine Learning.
Fahad Ahmad.
Oct 28, 2021.
Top javaScript libraries for Machine learning
1. TensorFlow.js
TensorFlow.js is a JavaScript library for training and deploying models in the browser and on Node.js. TensorFlow.js in 2019 has become the bread and butter for all Machine Learning Javascript projects due to its comprehensive linear algebra core and deep learning layers.
It has rapidly caught up with its Python sister in the number of supported APIs and almost any problems in Machine Learning can be solved using it at this point.
TensorFlow.js can be used directly in the browsers while leveraging WebGL for accelerations.
2. Neuro.js
Neuro.js is a machine learning framework for building AI assistants and chat-bots.
Neuro is a library for developing and training ML models in JavaScript, and deploying them in the browser or on Node.js
3. Synaptic.js
Synaptic is a JavaScript neural network library for Node.js and the browser, its generalized algorithm is architecture-free, so you can build and train basically any type of first-order or even second-order neural network architecture.
This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so you can easily test and compare the performance of different architectures.
4. Brain.js
Brain.js: GPU accelerated Neural Networks in JavaScript for browsers and Node.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.
5. Keras.js
Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.
Basic Convent for MNIST
Convolutional Variational Autoencoder, trained on MNIST
Auxiliary Classifier Generative Adversarial Networks (AC-GAN) on MNIST
50-layer Residual Network, trained on ImageNet
Inception v3, trained on ImageNet
DenseNet-121, trained on ImageNet
SqueezeNet v1.1, trained on ImageNet
Bidirectional LSTM for IMDB sentiment classification
6 . Neatptic.js
Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
7. Mind
Flexible neural networks in JavaScript.
Mind lets you easily create networks that learn to make predictions.
8. ConvNetJS
ConvNetJS is a JavaScript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you’re training. No software requirements, no compilers, no installations, no GPUs, no sweat.
9 . ml.js
ml.js is a comprehensive, general-purpose JavaScript ML library for browsers and Node.js.It provides straightforward and mission-critical models and utilities for supervised and unsupervised problems. Focusing on the simplicity and all-in-one general-purpose machine learning for JavaScript and TypeScript developers, it provides clustering, decomposition, ensemble, bagging, linear models, feature extractions, and more.
10. math.js
Math.js is an extensive math library for JavaScript and Node.js. It features a flexible expression parser with support for symbolic computation, comes with a large set of built-in functions and constants, and offers an integrated solution to work with different data types like numbers, big numbers, complex numbers, fractions, units, and matrices. Powerful and easy to use.
Conclusion
In this article, we look at the 10 best open-source JavaScript machine learning libraries to extend JavaScript’s capabilities.
Further, as a developer, having and using the right JavaScript machine learning Libraries will help you in the quest for putting together an algorithm that will tap into the strengths and capabilities of the machine learning project of your choice.
This article just covers the tip of the JavaScript Machine Learning libraries.
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