Documentation changes to enhance readability

Documentation changes Integrated_gradients.ipynb

Author: vijayasarathib

site/en/tutorials/interpretability/integrated_gradients.ipynb

@@ -73,7 +73,7 @@
       "source": [
         "This tutorial demonstrates how to implement **Integrated Gradients (IG)**, an [Explainable AI](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence) technique introduced in the paper [Axiomatic Attribution for Deep Networks](https://arxiv.org/abs/1703.01365). IG aims to explain the relationship between a model's predictions in terms of its features. It has many use cases including understanding feature importances, identifying data skew, and debugging model performance.\n",
         "\n",
-        "IG has become a popular interpretability technique due to its broad applicability to any differentiable model (e.g. images, text, structured data), ease of implementation, theoretical justifications, and computational efficiency relative to alternative approaches that allows it to scale to large networks and feature spaces such as images.\n",
+        "IG has become a popular interpretability technique due to its broad applicability to any differentiable model (e.g. images, text, structured data), ease of implementation, theoretical justifications, and computational efficiency relative to alternative approaches that allow it to scale to large networks and feature spaces such as images.\n",
         "\n",
         "In this tutorial, you will walk through an implementation of IG step-by-step to understand the pixel feature importances of an image classifier. As an example, consider this [image](https://commons.wikimedia.org/wiki/File:San_Francisco_fireboat_showing_off.jpg) of a fireboat spraying jets of water. You would classify this image as a fireboat and might highlight the pixels making up the boat and water cannons as being important to your decision. Your model will also classify this image as a fireboat later on in this tutorial; however, does it highlight the same pixels as important when explaining its decision?\n",
         "\n",
@@ -151,7 +151,7 @@
         "\n",
         "**Inputs**: The expected input shape for the model is `(None, 224, 224, 3)`. This is a dense 4D tensor of dtype float32 and shape `(batch_size, height, width, RGB channels)` whose elements are RGB color values of pixels normalized to the range [0, 1]. The first element is `None` to indicate that the model can take any integer batch size.\n",
         "\n",
-        "**Outputs**: A `tf.Tensor` of logits in the shape of `(batch_size, 1001)`. Each row represents the model's predicted score for each of 1,001 classes from ImageNet. For the model's top predicted class index you can use `tf.math.argmax(predictions, axis=-1)`. Furthermore, you can also convert the model's logit output to predicted probabilities across all classes using `tf.nn.softmax(predictions, axis=-1)` to quantify the model's uncertainty as well as explore similar predicted classes for debugging."
+        "**Outputs**: A `tf.Tensor` of logits in the shape of `(batch_size, 1001)`. Each row represents the model's predicted score for 1,001 classes from ImageNet. For the model's top predicted class index you can use `tf.math.argmax(predictions, axis=-1)`. Furthermore, you can also convert the model's logit output to predicted probabilities across all classes using `tf.nn.softmax(predictions, axis=-1)` to quantify the model's uncertainty as well as explore similar predicted classes for debugging."
       ]
     },
     {
@@ -249,7 +249,7 @@
       },
       "source": [
         "### Classify images\n",
-        "Let's start by classifying these images and displaying the top 3 most confident predictions. Following is a utility function to retrieve the top k predicted labels and probabilities."
+        "Let's start by classifying these images and displaying the top 3 most confident predictions. The following is a utility function to retrieve the top k predicted labels and probabilities."
       ]
     },
     {