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MXNet vs Theano

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MXNet
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80
+ 1
2
Theano
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MXNet vs Theano: What are the differences?

MXNet: A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly; Theano: Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme.

MXNet and Theano can be primarily classified as "Machine Learning" tools.

MXNet and Theano are both open source tools. It seems that MXNet with 17.5K GitHub stars and 6.21K forks on GitHub has more adoption than Theano with 8.87K GitHub stars and 2.5K GitHub forks.

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    What is MXNet?

    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

    What is Theano?

    Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

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    What companies use MXNet?
    What companies use Theano?
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    What tools integrate with MXNet?
    What tools integrate with Theano?

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    What are some alternatives to MXNet and Theano?
    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/
    Gluon
    A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
    NumPy
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
    See all alternatives

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