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ML Kit
ML Kit

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ml5.js
ml5.js

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ml5.js vs ML Kit: 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; ML Kit: Machine learning for mobile developers (by Google). ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

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

ml5.js is an open source tool with 2.72K GitHub stars and 213 GitHub forks. Here's a link to ml5.js's open source repository on GitHub.

- No public GitHub repository available -

What is ML Kit?

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

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.
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                What are some alternatives to ML Kit and ml5.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.
                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.
                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.
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