JavaScript-based Frameworks Implemented in AI

The extreme growth in new technologies in the field of machine learning has helped software developers build new AI applications in ways easier than ever.

In the present day, most AI enthusiasts leverage Python frameworks for AI & machine learning development. But looking around, one may also find that JavaScript-based frameworks are also being implemented in AI.

This interesting intersection led us to explore and experiment with the odd possibilities of using Javascript and Machine Learning together. Sharing from our research, here are some neat JavaScript machine learning libraries that bring Javascript, Machine Learning, DNN, and even NLP together. Take a look.

1. Tensorflow.js
2. Brain.js
3. ml.js
4. stdlib
5. ConvNetJS
6. Neataptic
7. Synaptic
8. KERAS.JS
9. Math.js
10. Limdu.js
11. Neuro.js
12. Deeplearn.js
13. Apache MXNetjs
14. Synapses
15. Compromise

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1. Tensorflow.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. The Tensorflow.js model of supporting both browsers and Node.js environments has been adopted by many open-source libraries including brain.js and machinelearn.js.

Pros

• It has better computational graph visualizations.
• It has the advantage of seamless performance, quick updates and frequent new releases with new features.
• It can be deployed on a gamut of hardware machines, starting from cellular devices to computers with complex setups.
• Tensorflow is highly parallel and designed to use various backends software (GPU, ASIC), etc.

Cons

• No support for Windows
• Missing Symbolic Loops
• No GPU support other than Nvidia and only language support

2. brain.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.

Pros

• It is great for quickly creating a simple Neural Networks (NN) in a high-level language where you can take advantage of the huge number of open source libraries.
• With a good dataset and a few lines of code, you can create some really interesting functionality.
• It has the ability to run on client-side javascript.

Cons

• It limits your network architecture to a point where you can only do simple applications
• There isn’t much of a possibility for softmax layers or other structures.

3. 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.

Pros

• It allows Bit operations on arrays, hash tables, sorting, random number generation.
• It offers routine for linear algebra, array manipulation, and optimizations.
• It supports cross-validation.

Cons

• It has limited support for hardware acceleration.
• It does not have default access to the file system in the browser host environment.

#machine-learning #ai #javascript #developer