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Deepo

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Propel

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Deepo vs Propel: What are the differences?

Deepo: A Docker image containing almost all popular deep learning frameworks. Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch; Propel: Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

Deepo and Propel can be categorized as "Machine Learning" tools.

Deepo and Propel are both open source tools. Deepo with 4.92K GitHub stars and 578 forks on GitHub appears to be more popular than Propel with 2.81K GitHub stars and 81 GitHub forks.

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What is Deepo?

Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch.

What is Propel?

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

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    What tools integrate with Deepo?
    What tools integrate with Propel?
    What are some alternatives to Deepo 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.
    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.
    See all alternatives