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Best Deep Learning Frameworks and Tutorials

Discussion in 'Deep Learning Resources' started by self-driving, Jan 8, 2017.

  1. self-driving

    self-driving Administrator Staff Member

    This is a list of Deep Learning Frameworks provided by Nvidia. Almost all of them require python, so if you're not familiar with programming languages, Python could be a good start.
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    Caffe is a deep learning framework made with expression, speed, and modularity in mind. Caffe is developed by the Berkeley Vision and Learning Center (BVLC), as well as community contributors and is popular for computer vision.

    Caffe supports cuDNN v5 for GPU acceleration.

    Supported interfaces: C, C++, Python, MATLAB, Command line interface

    Learning Resources



    [​IMG] The Microsoft Cognitive Toolkit —previously known as CNTK— is a unified deep-learning toolkit from Microsoft Research that makes it easy to train and combine popular model types across multiple GPUs and servers. Microsoft Cognitive Toolkit implements highly efficient CNN and RNN training for speech, image and text data.

    Microsoft Cognitive Toolkit supports cuDNN v5.1 for GPU acceleration.

    Supported interfaces: Python, C++, C# and Command line interface




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    TensorFlow is a software library for numerical computation using data flow graphs, developed by Google’s Machine Intelligence research organization.

    TensorFlow supports cuDNN v5.1 for GPU acceleration.

    Supported interfaces: C++, Python




    [​IMG] Theano is a math expression compiler that efficiently defines, optimizes, and evaluates mathematical expressions involving multi-dimensional arrays.

    Theano supports cuDNN v5 for GPU acceleration.

    Supported interfaces: Python

    Learning resources


    [​IMG] Torch is a scientific computing framework that offers wide support for machine learning algorithms.

    Torch supports cuDNN v5 for GPU acceleration.

    Supported interfaces: C, C++, Lua

    Learning resources


    [​IMG] MXnet is a deep learning framework designed for both efficiency and flexibility that allows you to mix the flavors of symbolic programming and imperative programming to maximize efficiency and productivity.

    MXnet supports cuDNN v3 for GPU acceleration.

    Supported Interfaces: Python, R, C++, Julia


    [​IMG]Chainer is a deep learning framework that’s designed on the principle of define-by-run. Unlike frameworks that use the define-and-run approach, Chainer lets you modify networks during runtime, allowing you to use arbitrary control flow statements.

    Chainer supports cuDNN v4 for GPU acceleration.

    Supported Interfaces: Python


    [​IMG] Keras is a minimalist, highly modular neural networks library, written in Python, and capable of running on top of either TensorFlow or Theano. Keras was developed with a focus on enabling fast experimentation.

    cuDNN version depends on the version of TensorFlow and Theano installed with Keras.



    Supported Interfaces: Python
  2. JanSchumann

    JanSchumann New Member

    Great sharing. I am playing around with Keras right now and find it easy to a certain degree.
  3. roy89

    roy89 New Member

    Thx for sharing. Will give a chance to Chainer ;)

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