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ml5.js

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TensorFlow.js

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ml5.js vs TensorFlow.js: What are the differences?

ml5.js: 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; TensorFlow.js: Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

ml5.js and TensorFlow.js belong to "Machine Learning Tools" category of the tech stack.

ml5.js and TensorFlow.js are both open source tools. TensorFlow.js with 11.1K GitHub stars and 801 forks on GitHub appears to be more popular than ml5.js with 2.63K GitHub stars and 209 GitHub forks.

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Pros of ml5.js
Pros of TensorFlow.js
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      NodeJS Powered
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      Deploy python ML model directly into javascript

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    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 TensorFlow.js?

    Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

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    What companies use ml5.js?
    What companies use TensorFlow.js?
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      What tools integrate with ml5.js?
      What tools integrate with TensorFlow.js?
        No integrations found
        What are some alternatives to ml5.js and TensorFlow.js?
        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.
        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.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        CUDA
        A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
        See all alternatives