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MXNet vs Stan

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MXNet
48
80
+ 1
2
Stan
64
27
+ 1
0
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MXNet vs Stan: What are the differences?

What is 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.

What is Stan? A Probabilistic Programming Language. A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.

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

MXNet and Stan are both open source tools. MXNet with 17.5K GitHub stars and 6.21K forks on GitHub appears to be more popular than Stan with 1.62K GitHub stars and 282 GitHub forks.

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Pros of MXNet
Pros of Stan
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    - No public GitHub repository available -

    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 Stan?

    A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.

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    Jobs that mention MXNet and Stan as a desired skillset
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    What companies use MXNet?
    What companies use Stan?
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    What tools integrate with MXNet?
    What tools integrate with Stan?

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    What are some alternatives to MXNet and Stan?
    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/
    Theano
    Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
    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.
    See all alternatives

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