Several spelling and grammar fixes

doc/sandbox/interactive_debugger.txt

@@ -8,7 +8,7 @@ Interactive Debugger
 
 The interactive debugger should allow the user to go step by step in a graph to debug it. It should allow setting breakpoints on arbitrary Ops or subgraphs. If we can group ops by the user's function that defined them, we could have a logical grouping of the graph into subgraphs.
 
-The debugger should save the inputs at each step so the user loses no info through inplace operations. Ideally, the debugger should be a normal python shell enrished with commands to control the flow and all the inputs should be made available so the user can use numpy interactively on them.
+The debugger should save the inputs at each step so the user loses no info through inplace operations. Ideally, the debugger should be a normal python shell enriched with commands to control the flow and all the inputs should be made available so the user can use numpy interactively on them.
 
 Command wishlist
  * py_perform (perform the current operation using the python implementation)

doc/tutorial/conditions.txt

@@ -9,7 +9,7 @@ Conditions
 - Build condition over symbolic variables.
 - IfElse Op takes a `boolean` condition and two variables to compute as input.
 - Switch take a `tensor` as condition and two variables to compute as input.
-  - Switch is an elemwise operation. It is more general then ifelse.
+  - Switch is an elementwise operation. It is more general than IfElse.
 - While Switch Op evaluates both 'output' variables, IfElse Op is lazy and only
   evaluates one variable respect to the condition.
 

doc/tutorial/examples.txt

@@ -366,4 +366,4 @@ For example:
 Others Random Distributions
 ---------------------------
 
-Threre is :ref:`others distribution implemented <libdoc_tensor_raw_random>`. 
+There are :ref:`other distributions implemented <libdoc_tensor_raw_random>`. 

doc/tutorial/gradients.txt

@@ -221,15 +221,15 @@ array([[ 0.,  0.],
     as the output.
 
 
-Hesian times a vector
+Hessian times a vector
 =====================
 
 If you need to compute the Hessian times a vector, you can make use of the
-above defined operators to do it more efficiently then actually computing
+above defined operators to do it more efficiently than actually computing
 the exact Hessian and then doing the product. Due to the symmetry of the 
 Hessian matrix, you have two options that will
-give you the same result (though might exhibit different performaces, so we
-suggest to profile the methods before using any of the two):
+give you the same result, though these options might exhibit different performance, so we
+suggest to profile the methods before using either of the two:
 
 
 >>> x = T.dvector('x')

doc/tutorial/loading_and_saving.txt

@@ -86,7 +86,7 @@ Short-term serialization
 
 If you are confident that the class instance you are serializing will be
 deserialized by a compatible version of the code, pickling the whole model is
-an adequate solution. It would be the cas, for instance, if you are saving
+an adequate solution. It would be the case, for instance, if you are saving
 models and reloading them during the same execution of your program, or if the
 class you're saving has been really stable for a while.
 

doc/tutorial/symbolic_graphs.txt

@@ -74,7 +74,7 @@ output. You can now print the name of the op that is applied to get
 'Elemwise{mul,no_inplace}'
 
 So a elementwise multiplication is used to compute ``y``. This
-muliplication is done between the inputs:
+multiplication is done between the inputs:
 
 >>> len(y.owner.inputs)
 2
@@ -126,7 +126,7 @@ When compiling a Theano function, what you give to the
 :func:`theano.function <function.function>` is actually a graph
 (starting from the outputs variables you can traverse the graph up to
 the input variables). While this graph structure shows how to compute
-the output from the input, it also offers the posibility to improve the  
+the output from the input, it also offers the possibility to improve the  
 way this computation is carried out. The way optimizations work in 
 Theano is by identifying and replacing certain patterns in the graph 
 with other specialized patterns that produce the same results but are either