Merge branch 'master' of git@github.com:lisa-lab/DeepLearningTutorials

Author: pascanur

doc/gettingstarted.txt

@@ -23,7 +23,7 @@ Datasets
 MNIST Dataset
 +++++++++++++
 
-(`mnist.pkl.gz <http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz>`_)
+(`mnist.pkl.gz <http://deeplearning.net/data/mnist/mnist.pkl.gz>`_)
 
  The `MNIST <http://yann.lecun.com/exdb/mnist>`_ dataset consists of handwritten 
  digit images and it is divided in 60 000 examples for the training set and 
@@ -48,7 +48,7 @@ MNIST Dataset
  .. |5| image:: images/mnist_5.png
 
  For convenience we pickled the dataset to make it easier to use in python.
- It is available for download `here <http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz>`_.
+ It is available for download `here <http://deeplearning.net/data/mnist/mnist.pkl.gz>`_.
  The pickled file represents a tuple of 3 lists : the training set, the 
  validation set and the testing set. Each of the three lists is a pair
  formed from a list of images and a list of class labels for each of the
@@ -667,7 +667,7 @@ another machine in a distributed job.
 Read more about `serialization in Theano`_, or Python's `pickling`_.
 
 .. _pickling: http://docs.python.org/library/pickle.html
-.. _serialization in Theano: http://deeplearning.net/theanodoc/tutorial/loading_and_saving.html
+.. _serialization in Theano: http://deeplearning.net/software/theano/tutorial/loading_and_saving.html
 
 Plotting Intermediate Results
 ++++++++++++++++++++++++++++++

doc/intro.txt

@@ -41,7 +41,7 @@ The unsupervised and semi-supervised learning algorithms are less co-dependent,
   * :ref:`encoder_decoder`
 
 
-.. _Theano: http://deeplearning.net/theanodoc
+.. _Theano: http://deeplearning.net/software/theano
 
-.. _Theano basic tutorial: http://deeplearning.net/theanodoc/tutorial
+.. _Theano basic tutorial: http://deeplearning.net/software/theano/tutorial
 

doc/logreg.txt

@@ -13,13 +13,13 @@ Classifying MNIST digits using Logistic Regression
 .. note::
     The code for this section is available for download `here`_.
 
-.. _here: http://www.iro.umontreal.ca/~lisa/deep/tutorial/code/logistic_sgd.py
+.. _here: http://deeplearning.net/tutorial/code/logistic_sgd.py
 
-.. _shared variables: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/examples.html#using-shared-variables
+.. _shared variables: http://deeplearning.net/software/theano/tutorial/examples.html#using-shared-variables
 
-.. _basic arithmetic ops: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/adding.html#adding-two-scalars
+.. _basic arithmetic ops: http://deeplearning.net/software/theano/tutorial/adding.html#adding-two-scalars
 
-.. _T.grad: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/examples.html#computing-gradients
+.. _T.grad: http://deeplearning.net/software/theano/tutorial/examples.html#computing-gradients
 
 In this section, we show how Theano can be used to implement the most basic
 classifier: the logistic regression. We start off with a quick primer of the
@@ -389,7 +389,7 @@ error of 7.489%. On the GPU the code does almost 10.0 epochs/sec.
 
 .. [#f1] For smaller datasets and simpler models, more sophisticated descent
          algorithms can be more effective. The sample code
-         `logistic_cg.py <http://www.iro.umontreal.ca/~lisa/deep/tutorial/code/logistic_cg.py>`_
+         `logistic_cg.py <http://deeplearning.net/tutorial/code/logistic_cg.py>`_
          demonstrates how to use SciPy's conjugate gradient solver with Theano 
          on the logistic regression task.
 

doc/mlp.txt

@@ -14,15 +14,15 @@ Multilayer Perceptron
 .. note::
     The code for this section is available for download `here`_.
 
-.. _here: http://www.iro.umontreal.ca/~lisa/deep/tutorial/code/mlp.py
+.. _here: http://deeplearning.net/tutorial/code/mlp.py
 
-.. _T.tanh: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/examples.html?highlight=tanh
+.. _T.tanh: http://deeplearning.net/software/theano/tutorial/examples.html?highlight=tanh
 
-.. _shared variables: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/examples.html#using-shared-variables
+.. _shared variables: http://deeplearning.net/software/theano/tutorial/examples.html#using-shared-variables
 
-.. _basic arithmetic ops: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/adding.html#adding-two-scalars
+.. _basic arithmetic ops: http://deeplearning.net/software/theano/tutorial/adding.html#adding-two-scalars
 
-.. _T.grad: http://www.iro.umontreal.ca/~lisa/deep/theanodoc/tutorial/examples.html#computing-gradients
+.. _T.grad: http://deeplearning.net/software/theano/tutorial/examples.html#computing-gradients
 
 
 The next architecture we are going to present using Theano is the single-hidden