Fix typos

assets/recipes_generation.en.md

@@ -1289,14 +1289,14 @@ _<small>➔ output:</small>_
 To get actual predictions from the model we need to sample from the output distribution, to get actual character indices. This distribution is defined by the logits over the character vocabulary.
 
 ```python
-print('Prediction for the 1st letter of the batch 1st sequense:')
+print('Prediction for the 1st letter of the batch 1st sequence:')
 print(example_batch_predictions[0, 0])
 ```
 
 _<small>➔ output:</small>_ 
 
 > ```text
-> Prediction for the 1st letter of the batch 1st sequense:
+> Prediction for the 1st letter of the batch 1st sequence:
 > tf.Tensor(
 > [-9.0643829e-03 -1.9503604e-03  9.3381782e-04  3.7442446e-03
 >  -2.0541784e-03 -7.4054599e-03 -7.1884273e-03  2.6014952e-03

assets/recipes_generation.ru.md

@@ -1301,14 +1301,14 @@ _➔ вывод:_
 Для того, чтобы выбрать символ, который по мнению модели должен идти следующим нам необходимо сделать sampling по вероятностям появления каждого символа.
 
 ```python
-print('Prediction for the 1st letter of the batch 1st sequense:')
+print('Prediction for the 1st letter of the batch 1st sequence:')
 print(example_batch_predictions[0, 0])
 ```
 
 _➔ вывод:_ 
 
 > ```text
-> Prediction for the 1st letter of the batch 1st sequense:
+> Prediction for the 1st letter of the batch 1st sequence:
 > tf.Tensor(
 > [-9.0643829e-03 -1.9503604e-03  9.3381782e-04  3.7442446e-03
 >  -2.0541784e-03 -7.4054599e-03 -7.1884273e-03  2.6014952e-03

experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb

@@ -2603,7 +2603,7 @@
     }
    ],
    "source": [
-    "# Let's do a quick detour and see how Embeding layer works.\n",
+    "# Let's do a quick detour and see how Embedding layer works.\n",
     "# It takes several char indices sequences (batch) as an input.\n",
     "# It encodes every character of every sequence to a vector of tmp_embeding_size length.\n",
     "tmp_vocab_size = 10\n",
@@ -2894,7 +2894,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Prediction for the 1st letter of the batch 1st sequense:\n",
+      "Prediction for the 1st letter of the batch 1st sequence:\n",
       "tf.Tensor(\n",
       "[-9.0643829e-03 -1.9503604e-03  9.3381782e-04  3.7442446e-03\n",
       " -2.0541784e-03 -7.4054599e-03 -7.1884273e-03  2.6014952e-03\n",
@@ -2944,7 +2944,7 @@
     }
    ],
    "source": [
-    "print('Prediction for the 1st letter of the batch 1st sequense:')\n",
+    "print('Prediction for the 1st letter of the batch 1st sequence:')\n",
     "print(example_batch_predictions[0, 0])"
    ]
   },

experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb

@@ -1417,7 +1417,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Prediction for the 1st letter of the batch 1st sequense:\n",
+      "Prediction for the 1st letter of the batch 1st sequence:\n",
       "tf.Tensor(\n",
       "[-5.5658985e-03 -5.6167855e-03  2.3333444e-03 -5.4010577e-03\n",
       " -1.2658490e-03 -2.0685978e-03 -1.7119508e-03 -1.9059415e-03\n",
@@ -1440,7 +1440,7 @@
     }
    ],
    "source": [
-    "print('Prediction for the 1st letter of the batch 1st sequense:')\n",
+    "print('Prediction for the 1st letter of the batch 1st sequence:')\n",
     "print(example_batch_predictions[0, 0])"
    ]
   },

experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb

@@ -1055,7 +1055,7 @@
     }
    ],
    "source": [
-    "# Map character indices to characters from vacabulary.\n",
+    "# Map character indices to characters from vocabulary.\n",
     "index2char = np.array(vocab)\n",
     "\n",
     "print(index2char)"
@@ -1365,7 +1365,7 @@
     "id": "BDYHEJ0pY1ai"
    },
    "source": [
-    "Each index of these vectors are processed as one time step. For the input at time step 0, the model receives the index for \"F\" and trys to predict the index for \"i\" as the next character. At the next timestep, it does the same thing but the RNN considers the previous step context in addition to the current input character."
+    "Each index of these vectors are processed as one time step. For the input at time step 0, the model receives the index for \"F\" and tries to predict the index for \"i\" as the next character. At the next timestep, it does the same thing but the RNN considers the previous step context in addition to the current input character."
    ]
   },
   {
@@ -1644,7 +1644,7 @@
     }
    ],
    "source": [
-    "# Let's do a quick detour and see how Embeding layer works.\n",
+    "# Let's do a quick detour and see how Embedding layer works.\n",
     "# It takes several char indices sequences (batch) as an input.\n",
     "# It encodes every character of every sequence to a vector of tmp_embeding_size length.\n",
     "tmp_vocab_size = 10\n",
@@ -1935,7 +1935,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Prediction for the 1st letter of the batch 1st sequense:\n",
+      "Prediction for the 1st letter of the batch 1st sequence:\n",
       "tf.Tensor(\n",
       "[-2.96991039e-03  2.02196068e-04  5.34047745e-03 -2.94846855e-03\n",
       " -3.64167639e-03 -2.63241702e-04 -8.80502281e-04  7.99844624e-04\n",
@@ -2097,7 +2097,7 @@
     }
    ],
    "source": [
-    "print('Prediction for the 1st letter of the batch 1st sequense:')\n",
+    "print('Prediction for the 1st letter of the batch 1st sequence:')\n",
     "print(example_batch_predictions[0, 0])"
    ]
   },