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Propel vs TensorFlow.js: What are the differences?
What is Propel? Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
What is 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.
Propel and TensorFlow.js can be primarily classified as "Machine Learning" tools.
Propel 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 Propel with 2.81K GitHub stars and 83 GitHub forks.
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Pros of TensorFlow.js
- NodeJS Powered4
- Open Source4
- Deploy python ML model directly into javascript1
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What is Propel?
Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
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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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.