:mod:`nolearn.lasagne`
----------------------

Two introductory tutorials exist for *nolearn.lasagne*:

- `Using convolutional neural nets to detect facial keypoints tutorial
  <http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/>`_
  with `code <https://github.com/dnouri/kfkd-tutorial>`_

- `Training convolutional neural networks with nolearn
  <http://nbviewer.ipython.org/github/dnouri/nolearn/blob/master/docs/notebooks/CNN_tutorial.ipynb>`_
  
For specifics around classes and functions out of the *lasagne*
package, such as layers, updates, and nonlinearities, you'll want to
look at the `Lasagne project's documentation
<http://lasagne.readthedocs.org/>`_.

*nolearn.lasagne* comes with a `number of tests
<https://github.com/dnouri/nolearn/tree/master/nolearn/lasagne/tests>`_
that demonstrate some of the more advanced features, such as networks
with merge layers, and networks with multiple inputs.

Finally, there's a few presentations and examples from around the web.
Note that some of these might need a specific version of nolearn and
Lasange to run:

- Oliver Dürr's `Convolutional Neural Nets II Hands On
  <https://home.zhaw.ch/~dueo/bbs/files/ConvNets_24_April.pdf>`_ with
  `code <https://github.com/oduerr/dl_tutorial/tree/master/lasagne>`_

- Roelof Pieters' presentation `Python for Image Understanding
  <http://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets>`_
  comes with nolearn.lasagne code examples

- Benjamin Bossan's `Otto Group Product Classification Challenge
  using nolearn/lasagne
  <https://github.com/ottogroup/kaggle/blob/master/Otto_Group_Competition.ipynb>`_

- Kaggle's `instructions on how to set up an AWS GPU instance to run
  nolearn.lasagne
  <https://www.kaggle.com/c/facial-keypoints-detection/details/deep-learning-tutorial>`_
  and the facial keypoint detection tutorial

- `An example convolutional autoencoder
  <https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb>`_

- Winners of the saliency prediction task in the 2015 `LSUN Challenge
  <http://lsun.cs.princeton.edu/>`_ have published their
  `lasagne/nolearn-based code
  <https://imatge.upc.edu/web/resources/end-end-convolutional-networks-saliency-prediction-software>`_.

API
~~~

.. automodule:: nolearn.lasagne

  .. autoclass:: NeuralNet
     :members:

  .. autoclass:: BatchIterator
     :members:

  .. autoclass:: TrainSplit
     :members: