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ML Kit
ML Kit

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Propel
Propel

2
10
+ 1
0
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ML Kit vs Propel: What are the differences?

What is 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.

What is Propel? Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

ML Kit and Propel can be primarily classified as "Machine Learning" tools.

Some of the features offered by ML Kit are:

  • Image labeling - Identify objects, locations, activities, animal species, products, and more
  • Text recognition (OCR) - Recognize and extract text from images
  • Face detection - Detect faces and facial landmarks

On the other hand, Propel provides the following key features:

  • Run anywhere, in the browser or natively from Node
  • Target multiple GPUs and make TCP connections
  • PhD optional

Propel is an open source tool with 2.81K GitHub stars and 81 GitHub forks. Here's a link to Propel'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 Propel?

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
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              What are some alternatives to ML Kit 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.
              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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