typos

6.1-one-hot-encoding-of-words-or-characters.ipynb

@@ -155,7 +155,7 @@
     "samples = ['The cat sat on the mat.', 'The dog ate my homework.']\n",
     "\n",
     "# We create a tokenizer, configured to only take\n",
-    "# into account the top-1000 most common on words\n",
+    "# into account the top-1000 most common words\n",
     "tokenizer = Tokenizer(num_words=1000)\n",
     "# This builds the word index\n",
     "tokenizer.fit_on_texts(samples)\n",

6.1-using-word-embeddings.ipynb

@@ -589,7 +589,7 @@
     "Additionally, we freeze the embedding layer (we set its `trainable` attribute to `False`), following the same rationale as what you are \n",
     "already familiar with in the context of pre-trained convnet features: when parts of a model are pre-trained (like our `Embedding` layer), \n",
     "and parts are randomly initialized (like our classifier), the pre-trained parts should not be updated during training to avoid forgetting \n",
-    "what they already know. The large gradient updated triggered by the randomly initialized layers would be very disruptive to the already \n",
+    "what they already know. The large gradient update triggered by the randomly initialized layers would be very disruptive to the already \n",
     "learned features."
    ]
   },