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Caffe

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Propel

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Propel vs Caffe: What are the differences?

Developers describe Propel as "Machine learning for JavaScript". Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript. On the other hand, Caffe is detailed as "A deep learning framework". It is a deep learning framework made with expression, speed, and modularity in mind.

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

Some of the features offered by Propel are:

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

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

  • Extensible code
  • Speed
  • Community

Propel and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than Propel with 2.8K GitHub stars and 80 GitHub forks.

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What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

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 Caffe 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.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
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.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
See all alternatives