Typo fix: slower then -> slower than

doc/library/tensor/basic.txt

@@ -1229,7 +1229,7 @@ Linear Algebra
     >>> result = batched_dot(first, second)
 
     :note:  This is a subset of numpy.einsum, but we do not provide it for now.
-    But numpy einsum is slower then dot or tensordot:
+    But numpy einsum is slower than dot or tensordot:
     http://mail.scipy.org/pipermail/numpy-discussion/2012-October/064259.html
 
     :param X: left term

theano/compile/profilemode.py

@@ -671,7 +671,7 @@ Test them first, as they are not guaranteed to always provide a speedup."""
         if not config.lib.amdlibm and any([exp_float32_op(a.op) and
                                            a.inputs[0].dtype == 'float32'
                                            for i, a in apply_time]):
-            print "  - With the default gcc libm, exp in float32 is slower then in float64! Try Theano flag floatX=float64, or install amdlibm and set the theano flags lib.amdlibm=True"
+            print "  - With the default gcc libm, exp in float32 is slower than in float64! Try Theano flag floatX=float64, or install amdlibm and set the theano flags lib.amdlibm=True"
             printed_tip = True
 
         #tip 4

theano/compile/profiling.py

@@ -824,7 +824,7 @@ if 0: # old code still to be ported from ProfileMode
 
         #tip 3
         if not config.lib.amdlibm and any([exp_float32_op(a.op) and a.inputs[0].dtype=='float32' for i,a in apply_time]):
-            print "  - With the default gcc libm, exp in float32 is slower then in float64! Try Theano flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True"
+            print "  - With the default gcc libm, exp in float32 is slower than in float64! Try Theano flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True"
 
         #tip 4
         for a, t in apply_time.iteritems():

theano/tensor/nnet/conv.py

@@ -162,7 +162,7 @@ class ConvOp(OpenMPOp):
 #It is an Intel(R) Xeon(R) CPU E5430 @ 2.66GHz. It is computer with theano/tensor/nnet/tests/speed_test_conv.py
 # and took 5 minutes to run.
 #TODO: we should compute this table for each computer/os as this can change.
-#      I saw on one computer that the speed with the shape can be slower then without!
+#      I saw on one computer that the speed with the shape can be slower than without!
 #      using the real shape and the same dtype could also help.
 
 #unroll_batch, unroll_kern, valid time, full time