docs: examples: notebooks: Fix JSON from when we added link to video …

…on setting up for ML tasks Signed-off-by: John Andersen <[email protected]>
intel · Oct 9, 2021 · 3c03add · 3c03add
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diff --git a/examples/notebooks/ensemble_by_stacking.ipynb b/examples/notebooks/ensemble_by_stacking.ipynb
@@ -7,7 +7,7 @@
     "\n",
     "In this demo, we'll be using the Red Wine Quality dataset. The dataset can be used with both regression and classification models. \n",
     "\n",
-    "The purpose of this notebook is to build different models and use stacking, an ensemble learning technique, using the DFFML API."
+    "The purpose of this notebook is to build different models and use stacking, an ensemble learning technique, using the DFFML API.",
     "\n",
     "[![Video Explanation Thumbnail](http://img.youtube.com/vi/PCoSP-jO9Y8/0.jpg)](http://www.youtube.com/watch?v=PCoSP-jO9Y8)"
    ],

diff --git a/examples/notebooks/multioutput_models.ipynb b/examples/notebooks/multioutput_models.ipynb
@@ -5,7 +5,7 @@
    "source": [
     "# Working with Multi-Output models\n",
     "\n",
-    "In this notebook, we'll be performing multi-output tasks with the help of DFFML. DFFML makes it so that users can use a range of different models to perform multi-output tasks with the usual worklow, but instantiating models with a list of targets/labels!"
+    "In this notebook, we'll be performing multi-output tasks with the help of DFFML. DFFML makes it so that users can use a range of different models to perform multi-output tasks with the usual worklow, but instantiating models with a list of targets/labels!",
     "\n",
     "[![Video Explanation Thumbnail](http://img.youtube.com/vi/7whKBThZDN4/0.jpg)](http://www.youtube.com/watch?v=7whKBThZDN4)"
    ],

diff --git a/examples/notebooks/saving_and_loading_models.ipynb b/examples/notebooks/saving_and_loading_models.ipynb
@@ -5,7 +5,7 @@
    "source": [
     "# Saving and loading models\n",
     "\n",
-    "In this notebook, we'll be using the red wine quality data set. The purpose of this notebook is to show how to save a trained model and load it again in a fresh session."
+    "In this notebook, we'll be using the red wine quality data set. The purpose of this notebook is to show how to save a trained model and load it again in a fresh session.",
     "\n",
     "[![Video Explanation Thumbnail](http://img.youtube.com/vi/ktVYFIH1K1g/0.jpg)](http://www.youtube.com/watch?v=ktVYFIH1K1g)"
    ],

diff --git a/examples/notebooks/transferlearning.ipynb b/examples/notebooks/transferlearning.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "2.    **Fine-Tuning the CNN**: Unfreezing the weights of the top layers of a frozen model base and jointly training both the newly-added classifier layers and the last layers of the base model. This is done by setting the parameter `trainable = True`. This part of the tutorial will also focus on showing how to add custom layers to the model to perform fine-tuning of the CNN.\n",
     "\n",
-    "We will be applying the first approach in this tutorial, ie. **Use CNN for Feature Extraction**. In this approach the only difference from **Fine-Tuning the CNN** is essentially setting `trainable = False` rather than `True`. Feel free to try and change the bool value as you please."
+    "We will be applying the first approach in this tutorial, ie. **Use CNN for Feature Extraction**. In this approach the only difference from **Fine-Tuning the CNN** is essentially setting `trainable = False` rather than `True`. Feel free to try and change the bool value as you please.",
     "\n",
     "[![Video Explanation Thumbnail](http://img.youtube.com/vi/xb3h_S8abZw/0.jpg)](http://www.youtube.com/watch?v=xb3h_S8abZw)"
    ],

diff --git a/examples/notebooks/tuning_models.ipynb b/examples/notebooks/tuning_models.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Tuning Models"
+    "# Tuning Models",
     "\n",
     "[![Video Explanation Thumbnail](http://img.youtube.com/vi/oo_SK7AHO4Y/0.jpg)](http://www.youtube.com/watch?v=oo_SK7AHO4Y)"
    ],