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ml5.js
ml5.js

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Propel

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ml5.js vs Propel: What are the differences?

Developers describe ml5.js as "Friendly machine learning for the web". ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies. On the other hand, Propel is detailed as "Machine learning for JavaScript". Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

ml5.js and Propel can be primarily classified as "Machine Learning" tools.

ml5.js and Propel are both open source tools. It seems that Propel with 2.81K GitHub stars and 81 forks on GitHub has more adoption than ml5.js with 2.72K GitHub stars and 213 GitHub forks.

What is ml5.js?

ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

What is Propel?

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
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                What are some alternatives to ml5.js and Propel?
                TensorFlow
                TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
                scikit-learn
                scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
                Keras
                Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
                PyTorch
                PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
                ML Kit
                ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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