Fix several typos
diff --git a/examples/lstm_seq2seq.py b/examples/lstm_seq2seq.py
index a53a3dcde66..2754561df48 100644
--- a/examples/lstm_seq2seq.py
+++ b/examples/lstm_seq2seq.py
@@ -10,7 +10,7 @@
 # Summary of the algorithm
 
 - We start with input sequences from a domain (e.g. English sentences)
-    and correspding target sequences from another domain
+    and corresponding target sequences from another domain
     (e.g. French sentences).
 - An encoder LSTM turns input sequences to 2 state vectors
     (we keep the last LSTM state and discard the outputs).
diff --git a/keras/backend/tensorflow_backend.py b/keras/backend/tensorflow_backend.py
index 346d0b88662..9716eb0bc4f 100644
--- a/keras/backend/tensorflow_backend.py
+++ b/keras/backend/tensorflow_backend.py
@@ -2736,7 +2736,7 @@ def rnn(step_function, inputs, initial_states,
                 states: List of tensors.
             Returns:
                 outputs: Tensor with shape (samples, ...) (no time dimension),
-                new_states: Tist of tensors, same length and shapes
+                new_states: List of tensors, same length and shapes
                     as 'states'.
         inputs: Tensor of temporal data of shape (samples, time, ...)
             (at least 3D).
diff --git a/keras/backend/theano_backend.py b/keras/backend/theano_backend.py
index f91555481b1..3f70d826c88 100644
--- a/keras/backend/theano_backend.py
+++ b/keras/backend/theano_backend.py
@@ -1323,7 +1323,7 @@ def rnn(step_function, inputs, initial_states,
                 states: List of tensors.
             Returns:
                 outputs: Tensor with shape (samples, ...) (no time dimension),
-                new_states: Tist of tensors, same length and shapes
+                new_states: List of tensors, same length and shapes
                     as 'states'.
         inputs: Tensor of temporal data of shape (samples, time, ...)
             (at least 3D).
diff --git a/keras/engine/network.py b/keras/engine/network.py
index 5304e706822..46c69795e7a 100644
--- a/keras/engine/network.py
+++ b/keras/engine/network.py
@@ -131,7 +131,7 @@ def _base_init(self, name=None):
         # Entries are unique. Includes input and output layers.
         self._layers = []
 
-        # Used only in conjonction with graph-networks
+        # Used only in conjunction with graph-networks
         self._outbound_nodes = []
         self._inbound_nodes = []
 
@@ -524,7 +524,7 @@ def input_spec(self):
                 or a single instance if the model has only one input.
         """
         if not self._is_graph_network:
-            # TODO: support it in subclassd networks after inputs are set.
+            # TODO: support it in subclassed networks after inputs are set.
             return None
 
         specs = []
diff --git a/keras/engine/saving.py b/keras/engine/saving.py
index c8fcc1046f1..5580d5df703 100644
--- a/keras/engine/saving.py
+++ b/keras/engine/saving.py
@@ -833,7 +833,7 @@ def _need_convert_kernel(original_backend):
     The convolution operation is implemented differently in different backends.
     While TH implements convolution, TF and CNTK implement the correlation operation.
     So the channel axis needs to be flipped when we're loading TF weights onto a TH model,
-    or vice verca. However, there's no conversion required between TF and CNTK.
+    or vice versa. However, there's no conversion required between TF and CNTK.
 
     # Arguments
         original_backend: Keras backend the weights were trained with, as a string.

Just fixing several typos, the first three of which are visible in the keras.io documentation.