typos reported by Ivo Danihelka

doc/introduction.txt

@@ -98,7 +98,7 @@ package, so what does Theano do that Python and numpy do not?
   parts your expression graph into CPU or GPU instructions, which run
   much faster than pure Python.
 
-- *symbolic differentiation*: Theano can automatic build symbolic graphs
+- *symbolic differentiation*: Theano can automatically build symbolic graphs
   for computing gradients.
 
 - *stability optimizations*: Theano can recognize [some] numerically unstable

doc/tutorial/debug_faq.txt

@@ -45,7 +45,7 @@ How do I print a graph (before or after compilation)?
 ----------------------------------------------------------
 
 Theano provides two functions (:func:`theano.pp` and
-:func:`theano.debugprint`) to print a graph to the terminal before or after
+:func:`theano.printing.debugprint`) to print a graph to the terminal before or after
 compilation.  These two functions print expression graphs in different ways:
 :func:`pp` is more compact and math-like, :func:`debugprint` is more verbose.
 Theano also provides :func:`pydotprint` that creates a png image of the function.

doc/tutorial/examples.txt

@@ -223,7 +223,7 @@ internal state, and returns the old state value.
 >>> accumulator = function([inc], state, updates=[(state, state+inc)])
 
 This code introduces a few new concepts.  The ``shared`` function constructs
-so-called :term:shared variables:.  These are hybrid symbolic and non-symbolic
+so-called :term:`shared variables`.  These are hybrid symbolic and non-symbolic
 variables.  Shared variables can be used in symbolic expressions just like
 the objects returned by ``dmatrices(...)`` but they also have a ``.value``
 property that defines the value taken by this symbolic variable in *all* the

doc/tutorial/modes.txt

@@ -48,7 +48,7 @@ expressions or new optimizations) to run your code using the DebugMode
 do several self-checks and assertations that can help to diagnose 
 possible programming errors that can lead to incorect output. Note that 
 ``DEBUG_MODE`` is much slower then ``FAST_RUN`` or ``FAST_COMPILE`` so 
-use it only during development (not when you luch 1000 process on a 
+use it only during development (not when you lunch 1000 process on a 
 cluster!).
 
 

doc/tutorial/numpy.txt

@@ -21,7 +21,7 @@ Matrix conventions for machine learning
 Rows are horizontal and columns are vertical.
 Every row is an example. Therefore, inputs[10,5] is a matrix of 10 examples 
 where each example has dimension 5. If this would be the input of a
-neural network then the weights from the input the the first hidden
+neural network then the weights from the input to the first hidden
 layer would represent a matrix of size (5, #hid). 
 
 If I have an array: