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|rss_image|  **2015-12 - 1/1** :ref:`Blog <ap-main-0>` :ref:`article (8) <ap-cat-article-0>` :ref:`articles (3) <ap-cat-articles-0>` :ref:`cours (6) <ap-cat-cours-0>` :ref:`module (7) <ap-cat-module-0>` :ref:`paper (3) <ap-cat-paper-0>`

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.. index:: 2015-12


.. _ap-month-2015-12-0:

2015-12 - 1/1
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.. blogpostagg::
    :title: Deep learning and other readings
    :date: 2015-12-22
    :keywords: machine learning,dask,OSM,Open Street Map
    :categories: deep learning
    :rawfile: 2015/2015-12-22_deeplearning.rst

    I came accross the following article
    `Evaluation of Deep Learning Toolkits <https://github.com/zer0n/deepframeworks/blob/master/README.md>`_
    which studies a short list of libraries for deep learning: Caffe, CNTK, TensorFlow, Theano, Torch,
    and various angles: modeling capability, interfaces, model deployment, performance, architecture, ecosystem, cross-platform.
    It gives a nice overview and helps choosing the library which fits your needs. Once your deep models has been trained,
    how to use it? This question should be the first one to be answered.
    

    ...





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|rss_image|  **2015-12 - 1/1** :ref:`2022-10 (1) <ap-month-2022-10-0>` :ref:`2022-12 (2) <ap-month-2022-12-0>` :ref:`2023-01 (1) <ap-month-2023-01-0>` :ref:`2023-02 (1) <ap-month-2023-02-0>` :ref:`2023-04 (1) <ap-month-2023-04-0>`