typo following code review.

theano/sparse/tests/test_utils.py

@@ -30,7 +30,7 @@ def test_hash_from_sparse():
 
             hashs.append(hash_from_sparse(data))
 
-        # test that different type of views and there copy give the same hash
+        # test that different type of views and their copy give the same hash
         assert hash_from_sparse(rng[1:]) == hash_from_sparse(rng[1:].copy())
         assert hash_from_sparse(rng[1:3]) == hash_from_sparse(rng[1:3].copy())
         assert hash_from_sparse(rng[:4]) == hash_from_sparse(rng[:4].copy())

theano/sparse/utils.py

@@ -3,7 +3,7 @@ from theano.gof.cc import hash_from_code
 
 def hash_from_sparse(data):
     # We need to hash the shapes as hash_from_code only hash
-    # the data buffer. Otherwise, this will cause problem with shapes likes:
+    # the data buffer. Otherwise, this will cause problem with shapes like:
     # (1, 0) and (2, 0)
     # We also need to add the dtype to make the distinction between
     # uint32 and int32 of zeros with the same shape.

theano/tensor/tests/test_utils.py

@@ -25,7 +25,7 @@ def test_hash_from_ndarray():
 
     assert len(set(hashs)) == len(hashs)
 
-    # test that different type of views and there copy give the same hash
+    # test that different type of views and their copy give the same hash
     assert hash_from_ndarray(rng[1:]) == hash_from_ndarray(rng[1:].copy())
     assert hash_from_ndarray(rng[1:3]) == hash_from_ndarray(rng[1:3].copy())
     assert hash_from_ndarray(rng[:4]) == hash_from_ndarray(rng[:4].copy())

theano/tensor/utils.py

@@ -5,13 +5,13 @@ from theano.gof.cc import hash_from_code
 
 def hash_from_ndarray(data):
     # We need to hash the shapes and strides as hash_from_code only hash
-    # the data buffer. Otherwise, this will cause problem with shapes likes:
+    # the data buffer. Otherwise, this will cause problem with shapes like:
     # (1, 0) and (2, 0) and problem with inplace transpose.
     # We also need to add the dtype to make the distinction between
     # uint32 and int32 of zeros with the same shape and strides.
 
-    # python hash are not strong, so I always use md5. To don't have a too long
-    # hash, I call it again on the contatenation of all part.
+    # python hash are not strong, so I always use md5 in order not to have a
+    # too long hash, I call it again on the concatenation of all parts.
     if not data.flags["C_CONTIGUOUS"] and not data.flags["F_CONTIGUOUS"]:
         data = numpy.ascontiguousarray(data)
     return (hash_from_code(hash_from_code(data) +