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Best NodeJS libraries and tools for machine learning
To help you understand better, let’s discuss some of the best NodeJS libraries and tools for machine learning.
If GPU isn’t available, BrainJS falls back to pure JS and continues computation. It offers numerous implementations on a neural network and encourages developing and building these neural nets on the server side with NodeJS. That is a major reason why a development agency uses this library for the simple execution of their machine learning projects.
- BrainJS helps create interesting functionality using fewer code lines and a reliable dataset.
- It’s a great library for quick development of a simple NN (Neural Network) wherein you can reap the benefits of accessing the wide variety of open-source libraries.
- There is not much possibility for a softmax layer or other such structures.
- It restricts the developer’s network architecture and only allows simple applications.
Cracking captcha with neural networks is a good example of a machine learning application that uses BrainJS.
- TensorflowJS can be implemented on several hardware machines, from computers to cellular devices with complicated setups
- It offers quick updates, frequent new features, releases, and seamless performance
- It has a better computational graph visualization
- TensorflowJS does not support Windows OS
- It has no GPU support besides Nvidia
NodeJS: Pitch Prediction is one of the best use cases for TensorflowJS.
Developers can use this library easily if they don’t know comprehensive details about machine learning techniques and neural networks. Synaptic also helps import and export ML models using JSON format. Besides, it comes with a few interesting pre-defined networks such as multi-layer perceptions, Hopfield networks, and LSTMs (long short-term memory networks).
- Synaptic can develop recurrent and second-order networks.
- It features pre-defined networks.
- There’s documentation available for layers, networks, neurons, architects, and trainers.
- Synaptic isn’t maintained actively anymore.
- It has a slow runtime compared to the other libraries.
MLJS offers mission-critical and straightforward utilities and models for unsupervised and supervised issues. It’s an easy-to-use, open-source library that can handle memory management in ML algorithms and GPU-based mathematical operations. The library supports other routines, too, like hash tables, arrays, statistics, cross-validation, linear algebra, etc.
- MLJS provides a routine for array manipulation, optimizations, and algebra
- It facilitates BIT operations on hash tables, arrays, and sorting
- MLJS extends support to cross-validation
- MLJS doesn’t offer default file system access in the host environment of the web browser
- It has restricted hardware acceleration support
Naïve-Bayes Classification is a good example that uses utilities from the MLJS library.
A major advantage of the NeuroJS library is that it provides support for real-time classification, online learning, and classification of multi-label forms while developing machine learning projects. The simple and performance-driven nature of this library makes machine learning practical and accessible to those using it.
- NeuroJS offers support for online learning and reinforcement learning
- It also supports the classification of multi-label forms
- NeuroJS does not support backpropagation and LSTM through time
A good example of NeuroJS being used along with React can be discovered here.
You can use this library to develop scalable, and modular APIs for other developers and yourself within minutes, sans having to tackle gateways, servers, domains, build SDKs, or write documentation.
- Stdlib offers robust, and rigorous statistical and mathematical functions
- It comes with auto-generated documentation
- The library offers easy-API access control and sharing
- Stdlib doesn’t support developing project builds that don’t feature runtime assertions.
- It does not support computing inverse hyperbolic secant.
Main, mk-stack, and From the Farmer, are three companies that reportedly use Stdlib in their technology stack.
- Using Keras, models can be trained in any backend
- It can exploit GPU support offered by the API of WebGL 3D designs
- The library is capable of running Keras models in programs
- Keras is not that useful if you wish to create your own abstract layer for research purposes
- It can only run in CPU mode
Moreover, developers having and utilizing the correct libraries and tools for machine learning jobs can help them put up algorithms and solutions capable of tapping the various strengths of their machine learning project.
We hope this article helps you learn and use the different libraries listed above in your project.