added pointers to ift6266 course notes on the same subjects

Author: Yoshua Bengio

doc/gettingstarted.txt

@@ -153,7 +153,7 @@ List of Symbols and acronyms
 
 * :math:`D`: number of input dimensions.
 * :math:`D_h^{(i)}`: number of hidden units in the :math:`i`-th layer.
-* :math:`f_{\theta}(x)`, :math:`f(x)`: classification function associated with a model :math:`P(Y|x,\theta)`, defined as :math:`argmax_k P(Y=k|x,\theta)`.
+* :math:`f_{\theta}(x)`, :math:`f(x)`: classification function associated with a model :math:`P(Y|x,\theta)`, defined as :math:`{\rm argmax}_k P(Y=k|x,\theta)`.
   Note that we will often drop the :math:`\theta` subscript.
 * L: number of labels.
 * :math:`\mathcal{L}(\theta, \cal{D})`: log-likelihood :math:`\cal{D}`
@@ -189,7 +189,9 @@ utility of unsupervised *pre-training* is often evaluated on the basis of what
 performance can be achieved after supervised *fine-tuning*.  This chapter
 reviews the basics of supervised learning for classification models, and covers
 the minibatch stochastic gradient descent algorithm that is used to fine-tune
-many of the models in the Deep Learning Tutorials.
+many of the models in the Deep Learning Tutorials. Have a look at these
+`introductory course notes on gradient-based learning <http://www.iro.umontreal.ca/~pift6266/H10/notes/gradient.html>`_
+for more basics on the notion of optimizing a training criterion using the gradient.
 
 
 .. _opt_learn_classifier:
@@ -228,7 +230,7 @@ In this tutorial, :math:`f` is defined as:
 
 .. math::
 
-    f(x) = argmax_k P(Y=k | x, \theta)
+    f(x) = {\rm argmax}_k P(Y=k | x, \theta)
 
 In python, using Theano this can be written as :
 

doc/intro.txt

@@ -2,6 +2,12 @@
 Deep Learning Tutorials
 =======================
 
+Deep Learning is a new area of Machine Learning research, which
+has been introduced with the objective of moving Machine Learning
+closer to one of its original goals: Artificial Intelligence.
+See these course notes for a `brief introduction to Machine Learning for AI <http://www.iro.umontreal.ca/~pift6266/H10/notes/mlintro.html>`_
+and an `introduction to Deep Learning algorithms <http://www.iro.umontreal.ca/~pift6266/H10/notes/deepintro.html>`_.
+
 Deep Learning is about learning multiple levels of representation
 and abstraction that help to
 make sense of data such as images, sound, and text. 
@@ -12,7 +18,7 @@ For more about deep learning algorithms, see for example:
  - The LISA `public wiki <http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/WebHome>`_ has a `reading list <http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/ReadingOnDeepNetworks>`_ and a `bibliography <http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepNetworksBibliography>`_.
  - Geoff Hinton has `readings <http://www.cs.toronto.edu/~hinton/deeprefs.html>`_ from last year's `NIPS tutorial <http://videolectures.net/jul09_hinton_deeplearn/>`_.
 
-These tutorials will introduce you to some of the most important deep learning
+The tutorials presented here will introduce you to some of the most important deep learning
 algorithms and will also show you how to run them using Theano_. Theano is a python library that makes writing deep learning models easy, and gives the option of
 training them on a GPU.
 

doc/mlp.txt

@@ -33,7 +33,8 @@ input data into a space where it becomes linearly separable. This intermediate
 layer is referred to as a **hidden layer**.  A single hidden layer is
 sufficient to make MLPs a **universal approximator**. However we will see later
 on that there are substantial benefits to using many such hidden layers, i.e. the
-very premise of **deep learning**.
+very premise of **deep learning**. See these course notes for an `introduction
+to MLPs, the back-propagation algorithm, and how to train MLPs <http://www.iro.umontreal.ca/~pift6266/H10/notes/mlp.html>`_.
 
 This tutorial will again tackle the problem of MNIST digit classification.