Update narrative content.

PiperOrigin-RevId: 394558889

g3doc/guide/_index.yaml

@@ -13,16 +13,21 @@ landing_page:
     - classname: devsite-landing-row-50
       description: >
         <p>
-        Preventing ML models from exposing potentially sensitive information is a critical part of
-        using AI responsibly. To that end, <i>differentially private stochastic gradient descent
-        (DP-SGD)</i> is a modification to the standard stochastic gradient descent (SGD) algorithm
-        in machine learning. </p>
-        <p>Models trained with DP-SGD have provable differential privacy (DP)
-        guarantees, mitigating the risk of exposing sensitive training data. Intuitively, a model
-        trained with differential privacy should not be affected by any single training example in
-        its data set. DP-SGD techniques can also be used in federated learning to provide user-level
-        differential privacy. You can learn more about differentially private deep learning in <a
-        href="https://arxiv.org/pdf/1607.00133.pdf">the original paper</a>.
+        An important aspect of responsible AI usage is ensuring that ML models are prevented from
+        exposing potentially sensitive information, such as demographic information or other
+        attributes in the training dataset that could be used to identify people.
+        One way to achieve this is by using differentially private stochastic gradient descent
+        (DP-SGD), which is a modification to the standard stochastic gradient descent (SGD)
+        algorithm in machine learning.
+        </p>
+        <p>
+        Models trained with DP-SGD have measurable differential privacy (DP) improvements, which
+        helps mitigate the risk of exposing sensitive training data. Since the purpose of DP is
+        to help prevent individual data points from being identified, a model trained with DP
+        should not be affected by any single training example in its training data set. DP-SGD
+        techniques can also be used in federated learning to provide user-level differential privacy.
+        You can learn more about differentially private deep learning in
+        <a href="https://arxiv.org/pdf/1607.00133.pdf">the original paper</a>.
         </p>
 
     - code_block: |
@@ -58,14 +63,19 @@ landing_page:
     items:
     - classname: devsite-landing-row-100
       description: >
-        <p>Tensorflow Privacy (TF Privacy) is an open source library developed by teams in Google
-        Research. The library includes implementations of commonly used TensorFlow Optimizers for
-        training ML models with DP. The goal is to enable ML practitioners using standard Tensorflow
-        APIs to train privacy-preserving models by changing only a few lines of code.</p>
-        <p> The differentially private Optimizers can be used in conjunction with high-level APIs
+        <p>
+        Tensorflow Privacy (TF Privacy) is an open source library developed by teams in
+        Google Research. The library includes implementations of commonly used TensorFlow
+        Optimizers for training ML models with DP. The goal is to enable ML practitioners
+        using standard Tensorflow APIs to train privacy-preserving models by changing only a
+        few lines of code.
+        </p>
+        <p>
+        The differentially private optimizers can be used in conjunction with high-level APIs
         that use the Optimizer class, especially Keras. Additionally, you can find differentially
         private implementations of some Keras models. All of the Optimizers and models can be found
-        in the <a href="./privacy/api">API Documentation</a>.</p>
+        in the <a href="./privacy/api_docs/python/tf_privacy">API Documentation</a>.</p>
+        </p>
 
   - classname: devsite-landing-row-cards
     items:

g3doc/guide/get_started.md

@@ -1,11 +1,12 @@
 # Get Started
 
+
 This document assumes you are already familiar with differential privacy, and
-have determined that you would like to implement TF Privacy to achieve
-differential privacy guarantees in your model(s). If you’re not familiar with
-differential privacy, please review
+have determined that you would like to use TF Privacy to implement differential
+privacy guarantees in your model(s). If you’re not familiar with differential
+privacy, please review
 [the overview page](https://tensorflow.org/responsible_ai/privacy/guide). After
-installing TF Privacy get started by following these steps:
+installing TF Privacy, get started by following these steps:
 
 ## 1. Choose a differentially private version of an existing Optimizer
 
@@ -36,9 +37,9 @@ microbatches.
 Train your model using the DP Optimizer (step 1) and vectorized loss (step 2).
 There are two options for doing this:
 
--   Pass the optimizer and loss as arguments to `Model.compile` before calling
+*   Pass the optimizer and loss as arguments to `Model.compile` before calling
     `Model.fit`.
--   When writing a custom training loop, use `Optimizer.minimize()` on the
+*   When writing a custom training loop, use `Optimizer.minimize()` on the
     vectorized loss.
 
 Once this is done, it’s recommended that you tune your hyperparameters. For a
@@ -65,7 +66,7 @@ The three new DP-SGD hyperparameters have the following effects and tradeoffs:
     utility because it lowers the standard deviation of the noise. However, it
     will slow down training in terms of time.
 2.  The clipping norm $C$: Since the standard deviation of the noise scales with
-    C, it is probably best to set $C$ to be some quantile (e.g. median, 75th
+    $C$, it is probably best to set $C$ to be some quantile (e.g. median, 75th
     percentile, 90th percentile) of the gradient norms. Having too large a value
     of $C$ adds unnecessarily large amounts of noise.
 3.  The noise multiplier $σ$: Of the three hyperparameters, the amount of

g3doc/guide/measure_privacy.md

@@ -2,12 +2,12 @@
 
 Differential privacy is a framework for measuring the privacy guarantees
 provided by an algorithm and can be expressed using the values ε (epsilon) and δ
-(delta). Of the two, ε is the more important and more sensitive to the choice of
+(delta). Of the two, ε is more important and more sensitive to the choice of
 hyperparameters. Roughly speaking, they mean the following:
 
 *   ε gives a ceiling on how much the probability of a particular output can
     increase by including (or removing) a single training example. You usually
-    want it to be a small constant (less than 10, or, for more stringent privacy
+    want it to be a small constant (less than 10, or for more stringent privacy
     guarantees, less than 1). However, this is only an upper bound, and a large
     value of epsilon may still mean good practical privacy.
 *   δ bounds the probability of an arbitrary change in model behavior. You can
@@ -30,17 +30,16 @@ dataset size and number of epochs. See the
 [classification privacy tutorial](../tutorials/classification_privacy.ipynb) to
 see the approach.
 
-For more detail, you can see
+For more detail, see
 [the original DP-SGD paper](https://arxiv.org/pdf/1607.00133.pdf).
 
-You can use `compute_dp_sgd_privacy`, to find out the epsilon given a fixed
-delta value for your model [../tutorials/classification_privacy.ipynb]:
+You can use `compute_dp_sgd_privacy` to find out the epsilon given a fixed delta
+value for your model [../tutorials/classification_privacy.ipynb]:
 
 *   `q` : the sampling ratio - the probability of an individual training point
     being included in a mini batch (`batch_size/number_of_examples`).
 *   `noise_multiplier` : A float that governs the amount of noise added during
     training. Generally, more noise results in better privacy and lower utility.
-    This generally
 *   `steps` : The number of global steps taken.
 
 A detailed writeup of the theory behind the computation of epsilon and delta is