:orphan:


.. _l-notebooks:

.. _notebooks-gallery:

Notebooks Gallery
=================



:ref:`l-notebooks-coverage`


.. contents::
    :depth: 1
    :local:

.. toctree::
    :maxdepth: 1

    notebooks/tree_to_onnx
    notebooks/convolutation_matmul
    notebooks/onnxruntime_training_nb

.. raw:: html

    <div class="sphx-pyq-thumb" tooltip="This notebook shows how to create a tree and execute it with onnx and onnxruntime. The direct way to do it is simple to use ONNX API and more precisely, the node TreeEnsembleRegressor. Another option is to create a tree in scikit-learn and then to convert it using skl2onnx.">

.. only:: html

    .. figure:: /notebooks/tree_to_onnx.thumb.png

        :ref:`treetoonnxrst`

.. raw:: html

    </div>


.. raw:: html

    <div class="sphx-pyq-thumb" tooltip="The convolution is a well known image transformation used to transform an image. It can be used to blur, to compute the gradient in one direction and it is widely used in deep neural networks. Having a fast implementation is important.">

.. only:: html

    .. figure:: /notebooks/convolutation_matmul.thumb.png

        :ref:`convolutationmatmulrst`

.. raw:: html

    </div>


.. raw:: html

    <div class="sphx-pyq-thumb" tooltip="onnxruntime-training is an extension onnxruntime or more precisely the same library compiled with different settings. It provides a way to compute a gradient of a function defined by an ONNX graph.">

.. only:: html

    .. figure:: /notebooks/onnxruntime_training_nb.thumb.png

        :ref:`onnxruntimetrainingnbrst`

.. raw:: html

    </div>




.. toctree::
    :hidden: 

    all_notebooks_coverage