Merge pull request #4728 from MarkDaoust/typos

Typos

Author: lamberta

samples/core/tutorials/eager/custom_training_walkthrough.ipynb

@@ -345,7 +345,7 @@
         "TensorFlow's [Dataset API](https://www.tensorflow.org/guide/datasets) handles many common cases for loading data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\n",
         "\n",
         "\n",
-        "Since the dataset is a CSV-formatted text file, use the the [make_csv_dataset](https://www.tensorflow.org/api_docs/python/tf/contrib/data/make_csv_dataset) function to parse the data into a suitable format. Since this function generates data for training models, the default behavior is to shuffle the data (`shuffle=True, shuffle_buffer_size=10000`), and repeat the dataset forever (`num_epochs=None`). We also set the [batch_size](https://developers.google.com/machine-learning/glossary/#batch_size) parameter."
+        "Since the dataset is a CSV-formatted text file, use the [make_csv_dataset](https://www.tensorflow.org/api_docs/python/tf/contrib/data/make_csv_dataset) function to parse the data into a suitable format. Since this function generates data for training models, the default behavior is to shuffle the data (`shuffle=True, shuffle_buffer_size=10000`), and repeat the dataset forever (`num_epochs=None`). We also set the [batch_size](https://developers.google.com/machine-learning/glossary/#batch_size) parameter."
       ]
     },
     {

samples/core/tutorials/keras/basic_regression.ipynb

@@ -115,7 +115,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "In a *regression* problem, we aim to predict the output of a continous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). \n",
+        "In a *regression* problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). \n",
         "\n",
         "This notebook builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. To do this, we'll provide the model with some data points about the suburb, such as the crime rate and the local property tax rate.\n",
         "\n",
@@ -342,7 +342,7 @@
       "source": [
         "## Create the model\n",
         "\n",
-        "Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output later that returns a single, continous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model, later on."
+        "Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output later that returns a single, continuous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model, later on."
       ]
     },
     {

samples/core/tutorials/keras/basic_text_classification.ipynb

@@ -512,7 +512,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "The layers are stacked sequentially to build the classifer:\n",
+        "The layers are stacked sequentially to build the classifier:\n",
         "\n",
         "1. The first layer is an `Embedding` layer. This layer takes the integer-encoded vocabulary and looks up the embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.\n",
         "2. Next, a `GlobalAveragePooling1D` layer returns a fixed-length output vector for each example by averaging over the sequence dimension. This allows the model can handle input of variable length, in the simplest way possible.\n",