Fixing some linter errors in TF documentation (Github > GitHub, the the > the).

PiperOrigin-RevId: 195386172

diff --git a/tensorflow/docs_src/deploy/index.md b/tensorflow/docs_src/deploy/index.md
index 07b1bc9..61edba0 100644 (file)
--- a/tensorflow/docs_src/deploy/index.md
+++ b/tensorflow/docs_src/deploy/index.md
@@ -14,4 +14,4 @@ the following documents:
     designed for production environments. TensorFlow Serving provides
     out-of-the-box integration with TensorFlow models.
     [Source code for TensorFlow Serving](https://github.com/tensorflow/serving)
-    is available on Github.
+    is available on GitHub.

diff --git a/tensorflow/docs_src/get_started/get_started_for_beginners.md b/tensorflow/docs_src/get_started/get_started_for_beginners.md
index fbe0ed7..d5a80e2 100644 (file)
--- a/tensorflow/docs_src/get_started/get_started_for_beginners.md
+++ b/tensorflow/docs_src/get_started/get_started_for_beginners.md
@@ -233,7 +233,7 @@ The Iris program requires the data from the following two .csv files:
 *   `http://download.tensorflow.org/data/iris_training.csv`, which contains
     the training set.
 *   `http://download.tensorflow.org/data/iris_test.csv`, which contains the
-    the test set.
+    test set.
 
 The **training set** contains the examples that we'll use to train the model;
 the **test set** contains the examples that we'll use to evaluate the trained

diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md
index c355300..f4b07db 100644 (file)
--- a/tensorflow/docs_src/mobile/android_build.md
+++ b/tensorflow/docs_src/mobile/android_build.md
@@ -26,7 +26,7 @@ If you haven't already, do the following two things:
 - Install [Android Studio](https://developer.android.com/studio/index.html),
   following the instructions on their website.
 
-- Clone the TensorFlow repository from Github:
+- Clone the TensorFlow repository from GitHub:
 
         git clone https://github.com/tensorflow/tensorflow
 
@@ -37,7 +37,7 @@ If you haven't already, do the following two things:
 
 2. From the **Open File or Project** window that appears, navigate to and select
     the `tensorflow/examples/android` directory from wherever you cloned the
-    TensorFlow Github repo.  Click OK.
+    TensorFlow GitHub repo.  Click OK.
 
     If it asks you to do a Gradle Sync, click OK.
 

diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md
index 2a0a77c..cf0db59 100644 (file)
--- a/tensorflow/docs_src/mobile/linking_libs.md
+++ b/tensorflow/docs_src/mobile/linking_libs.md
@@ -32,7 +32,7 @@ include this functionality in your program:
 2. Download the nightly precompiled version from
 [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/).
 
-3. Build the JAR file yourself using the instructions [in our Android Github repo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/android)
+3. Build the JAR file yourself using the instructions [in our Android GitHub repo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/android)
 
 ### iOS
 

diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index 69b63ae..1b0b9b4 100644 (file)
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -80,7 +80,7 @@ tracking is especially important for applications where you’re trying to count
 how many objects are present over time, since it gives you a good idea when a
 new object enters or leaves the scene. We have some sample code for this
 available for Android [on
-Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android),
+GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android),
 and also a [more general object detection
 model](https://github.com/tensorflow/models/tree/master/research/object_detection/README.md)
 available as well.
@@ -231,7 +231,7 @@ process.
 The next step is to pick an effective model to use. You might be able to avoid
 training a model from scratch if someone else has already implemented a model
 similar to what you need; we have a repository of models implemented in
-TensorFlow [on Github](https://github.com/tensorflow/models) that you can look
+TensorFlow [on GitHub](https://github.com/tensorflow/models) that you can look
 through. Lean towards the simplest model you can find, and try to get started as
 soon as you have even a small amount of labelled data, since you’ll get the best
 results when you’re able to iterate quickly. The shorter the time it takes to

diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md
index 11f11ea..01881cc 100644 (file)
--- a/tensorflow/docs_src/mobile/tflite/index.md
+++ b/tensorflow/docs_src/mobile/tflite/index.md
@@ -11,7 +11,7 @@ optimizing the kernels for mobile apps, pre-fused activations, and quantized
 kernels that allow smaller and faster (fixed-point math) models.
 
 Most of our TensorFlow Lite documentation is [on
-Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite)
+GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite)
 for the time being.
 
 ## What does TensorFlow Lite contain?