GitHub Link: github.com/BrainJS/brain.js
GitHub Link: github.com/stdlib/lib
The stdlib library also serves as a great all-in-one general purpose package that comes packed with extensive documentation, including several high-quality examples, tests, and benchmarks to help you start developing for Machine Learning.
GitHub Link: github.com/tensorflow/tfjs
All that tensorflow.js has to offer is broken into simple and efficient APIs, such as the core API, layers API, converter API, and more. The library also comes with built-in support for different backgrounds or platforms, such as WebGL, WebGPU, CPU backend, Node, to name a few. To give you an idea of its popularity, tensorflow.js is already being used in diverse sectors like education and healthcare.
GitHub Link: github.com/cghawthorne/deeplearnjs
The library offers two APIs, one is an immediate execution model like NumPy, while the other is a deferred execution model resembling the TensorFlow API. The deeplearnjs library was originally developed by the Google Brain’s PAIR team for the development of deep learning models and has been used for a variety of projects such as education, art, understanding models.
GitHub Link: github.com/machinelearnjs/machinelearnjs
At its core, machinelearn.js uses TensorFlow.js to provide an all-in-one library for ML developers with features, such as clustering, bagging, ensemble, linear models, feature extraction. Although the library is quite fast, you can enable acceleration using C++ binding or by using a GPU.
GitHub Link: github.com/stevenmiller888/mind
Mind’s website is home to an interesting implementation of the library in the form of a movie recommendation system that provides a hands-on experience and showcases its capabilities.
GitHub Link: github.com/karpathy/convnetjs
At the moment, ConvNetJS offers support for classification, regression (L2) cost functions, training Convolutional Networks for image processing, common Neural Network modules containing non-linearities and fully connected layers, and an experimental Reinforcement Learning module based-off Deep Q Learning. The library has some browser-based demos to give you an idea about it. But do keep in mind that the developer is not actively maintaining the library anymore.
GitHub Link: github.com/janhuenermann/neurojs
GitHub Link: github.com/mil-tokyo/webdnn
GitHub Link: github.com/transcranial/keras-js
The demos folder in their GitHub repo contains over 8 interactive demos based on real-world problems that can give you a hands-on idea of the capabilities and working of keras-js.
GitHub Link: github.com/cazala/synaptic
You can check out some of the implementations of synaptic, such as read from Wikipedia, self-organizing map, learn image filters, and more from the demos available on its GitHub page.
GitHub Link: github.com/spencermountain/compromise
The API provided by the compromise library includes a variety of useful functions, such as constructors, utilities, accessors, match, tags, loops, and much more for parsing and manipulating text. Apart from the functions, compromise also comes with a range of extensions that include adjectives functions, date functions, numbers functions among others. We suggest you take a look at the GitHub page to get the full picture.
• FlappyLearning • LimduJS
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