typos

Author: Yoshua Bengio

doc/rbm.txt

@@ -232,7 +232,7 @@ the Markov chain. What it means is that, for example, :math:`h^{(n+1)}_i` is
 randomly chosen to be 1 (versus 0) with probability :math:`sigm(W_i'x^{(n)} + c_i)`,
 and similarly, 
 :math:`x^{(n+1)}_j` is
-randomly chosen to be 1 (versus 0) with probability :math:`sigm(W_{.j}'h^{(n+1)} + b_j).
+randomly chosen to be 1 (versus 0) with probability :math:`sigm(W_{.j} h^{(n+1)} + b_j)`.
 
 This can be illustrated graphically:
 

doc/utilities.txt

@@ -15,7 +15,7 @@ Plotting Samples and Filters
 
 To plot a sample, what we need to do is to take the visible units, which 
 are a flattened image (there is no 2D structure to the visible units,
-just a 1D string of nodes) and reshape it into a 2D image. The order in
+just a 1D string of unit activations) and reshape it into a 2D image. The order in
 which the points from the 1D array go into the 2D image is given by the 
 order in which the inital MNIST images where converted into a 1D array.
 Lucky for us this is just a call of the ``numpy.reshape`` function.
@@ -30,12 +30,12 @@ the input image.
 
 We need a utility function that takes a minibatch, or the weight matrix, 
 and converts each row ( for the weight matrix we do a transpose ) into a 
-2D image and then tile this images together.  Once we converted the
+2D image and then tile these images together.  Once we converted the
 minibatch or the weights in this image of tiles, we can use PIL to plot 
 and save. `PIL <http://www.pythonware.com/products/pil/>`_ is a standard 
 python libarary to deal with images.
 
-Tiling minibatches together is done for us by 
+Tiling minibatches together is done for us by the 
 ``tile_raster_image`` function which we provide here. 
 
 .. code-block:: python