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numericaal
numericaal

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Propel

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numericaal vs Propel: What are the differences?

What is numericaal? Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

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

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

Some of the features offered by numericaal are:

  • MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.
  • CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.
  • BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.

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.

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What is numericaal?

numericaal automates model optimization and management so you can focus on data and training.

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 numericaal 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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