15 files changed, 61 insertions, 61 deletions
diff --git a/tensorflow/docs_src/about/bib.md b/tensorflow/docs_src/about/bib.md
index c9f0c532c6..5593a3d95c 100644
--- a/tensorflow/docs_src/about/bib.md
+++ b/tensorflow/docs_src/about/bib.md
@@ -60,7 +60,7 @@ author={
     Lukasz~Kaiser and
     Manjunath~Kudlur and
     Josh~Levenberg and
-    Dan~Man\'{e} and
+    Dandelion~Man\'{e} and
     Rajat~Monga and
     Sherry~Moore and
     Derek~Murray and
diff --git a/tensorflow/docs_src/api_guides/python/contrib.signal.md b/tensorflow/docs_src/api_guides/python/contrib.signal.md
index 85ef3ad134..0f7690f80a 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.signal.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.signal.md
@@ -28,14 +28,14 @@ The `axis` parameter to @{tf.contrib.signal.frame} allows you to frame tensors
 with inner structure (e.g. a spectrogram):
 
 ```python
-# `magnitude_spectrograms` is a [batch_size, ?, 127] tensor of spectrograms. We
+# `magnitude_spectrograms` is a [batch_size, ?, 129] tensor of spectrograms. We
 # would like to produce overlapping fixed-size spectrogram patches; for example,
 # for use in a situation where a fixed size input is needed.
 magnitude_spectrograms = tf.abs(tf.contrib.signal.stft(
     signals, frame_length=256, frame_step=64, fft_length=256))
 
-# `spectrogram_patches` is a [batch_size, ?, 64, 127] tensor containing a
-# variable number of [64, 127] spectrogram patches per batch item.
+# `spectrogram_patches` is a [batch_size, ?, 64, 129] tensor containing a
+# variable number of [64, 129] spectrogram patches per batch item.
 spectrogram_patches = tf.contrib.signal.frame(
     magnitude_spectrograms, frame_length=64, frame_step=16, axis=1)
 ```
diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md
index 45cb9d829c..dae50a8f03 100644
--- a/tensorflow/docs_src/api_guides/python/regression_examples.md
+++ b/tensorflow/docs_src/api_guides/python/regression_examples.md
@@ -229,4 +229,4 @@ passed through to the `model_fn` when the `model_fn` is called.
 The `model_fn` returns an
 @{tf.estimator.EstimatorSpec$`EstimatorSpec`} which is a simple structure
 indicating to the `Estimator` which operations should be run to accomplish
-varions tasks.
+various tasks.
diff --git a/tensorflow/docs_src/get_started/custom_estimators.md b/tensorflow/docs_src/get_started/custom_estimators.md
index 6343cc4ee4..42a246678a 100644
--- a/tensorflow/docs_src/get_started/custom_estimators.md
+++ b/tensorflow/docs_src/get_started/custom_estimators.md
@@ -15,7 +15,7 @@ git clone https://github.com/tensorflow/models/
 cd models/samples/core/get_started
 ```
 
-In this document we wil be looking at
+In this document we will be looking at
 [`custom_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py).
 You can run it with the following command:
 
@@ -161,7 +161,7 @@ classifier = tf.estimator.Estimator(
 
 To implement a typical model function, you must do the following:
 
-* (Define the model)[#define_the_model].
+* [Define the model](#define_the_model).
 * Specify additional calculations for each of
   the [three different modes](#modes):
   * [Predict](#predict)
diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/get_started/datasets_quickstart.md
index ecfbf160f0..a8a2ab6e56 100644
--- a/tensorflow/docs_src/get_started/datasets_quickstart.md
+++ b/tensorflow/docs_src/get_started/datasets_quickstart.md
@@ -169,7 +169,7 @@ the number of examples in the `Dataset` ensures that the data is completely
 shuffled. The Iris data set only contains 150 examples.
 
 The @{tf.data.Dataset.repeat$`repeat`} method has the `Dataset` restart when
-it reaches the end. To limit the number of epochss, set the `count` argument.
+it reaches the end. To limit the number of epochs, set the `count` argument.
 
 The @{tf.data.Dataset.repeat$`batch`} method collects a number of examples and
 stacks them, to create batches. This adds a dimension to their shape. The new
@@ -282,7 +282,7 @@ produce the necessary `(features, label)` pairs.
 
 We will start by building a function to parse a single line.
 
-The following `iris_data.parse_line` function acomplishes this taks using the
+The following `iris_data.parse_line` function accomplishes this task using the
 @{tf.decode_csv} function, and some simple python code:
 
 We must parse each of the lines in the dataset in order to generate the
diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md
index e3308ed716..ad3e1fe3e3 100644
--- a/tensorflow/docs_src/get_started/feature_columns.md
+++ b/tensorflow/docs_src/get_started/feature_columns.md
@@ -461,8 +461,8 @@ permitting a richer palette of numbers for every cell, an embedding column
 contains far fewer cells than an indicator column.
 
 Let's look at an example comparing indicator and embedding columns. Suppose our
-input examples consists of different words from a limited palette of only 81
-words. Further suppose that the data set provides provides the following input
+input examples consist of different words from a limited palette of only 81
+words. Further suppose that the data set provides the following input
 words in 4 separate examples:
 
 * `"dog"`
diff --git a/tensorflow/docs_src/get_started/premade_estimators.md b/tensorflow/docs_src/get_started/premade_estimators.md
index 45850a8996..4f01f997c3 100644
--- a/tensorflow/docs_src/get_started/premade_estimators.md
+++ b/tensorflow/docs_src/get_started/premade_estimators.md
@@ -372,7 +372,7 @@ Test set accuracy: 0.967
 
 We now have a trained model that produces good evaluation results.
 We can now use the trained model to predict the species of an Iris flower
-based on some unlabeled measurments. As with training and evaluation, we make
+based on some unlabeled measurements. As with training and evaluation, we make
 predictions using a single function call:
 
 ```python
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index ba1a4118ae..14add7c77e 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for macOS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 87cc647317..d2af9d9843 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 37e109a6e4..e5388c4b1e 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.5.0-rc1</version>
+  <version>1.5.0</version>
 </dependency>
 ```
 
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.5.0-rc1</version>
+                 <version>1.5.0</version>
                </dependency>
              </dependencies>
          </project>
@@ -123,12 +123,12 @@ instead:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow</artifactId>
-  <version>1.5.0-rc1</version>
+  <version>1.5.0</version>
 </dependency>
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow_jni_gpu</artifactId>
-  <version>1.5.0-rc1</version>
+  <version>1.5.0</version>
 </dependency>
 ```
 
@@ -147,7 +147,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or macOS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0-rc1.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0.zip).
   3. Extract this .zip file.
 
 
@@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.5.0-rc1.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.5.0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -239,11 +239,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and macOS X:
 
-<pre><b>java -cp libtensorflow-1.5.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.5.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.5.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.5.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 03f12dff08..cd8c14599f 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -31,13 +31,13 @@ If you are installing TensorFlow with GPU support using one of the
 mechanisms described in this guide, then the following NVIDIA software
 must be installed on your system:
 
-  * CUDA® Toolkit 8.0. For details, see
+  * CUDA® Toolkit 9.0. For details, see
     [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A).
     Ensure that you append the relevant Cuda pathnames to the
     `LD_LIBRARY_PATH` environment variable as described in the
     NVIDIA documentation.
-  * The NVIDIA drivers associated with CUDA Toolkit 8.0.
-  * cuDNN v6.0. For details, see
+  * The NVIDIA drivers associated with CUDA Toolkit 9.0.
+  * cuDNN v7.0. For details, see
     [NVIDIA's documentation](https://developer.nvidia.com/cudnn).
     Ensure that you create the `CUDA_HOME` environment variable as
     described in the NVIDIA documentation.
@@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -293,7 +293,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -480,7 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index e13ddadab7..f49d3a2f08 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -238,7 +238,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -347,7 +347,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl
 </pre>
 
 
@@ -528,5 +528,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
 </pre>
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 485863bf2e..bc7d2080dc 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -133,7 +133,7 @@ The following NVIDIA <i>hardware</i> must be installed on your system:
 
 The following NVIDIA <i>software</i> must be installed on your system:
 
-  * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 8.0.
+  * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 9.0.
     For details, see
     [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A).
     Ensure that you append the relevant Cuda pathnames to the
@@ -289,11 +289,11 @@ Do you wish to build TensorFlow with CUDA support? [y/N] <b>Y</b>
 CUDA support will be enabled for TensorFlow
 Do you want to use clang as CUDA compiler? [y/N]
 nvcc will be used as CUDA compiler
-Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: <b>8.0</b>
-Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
+Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: <b>9.0</b>
+Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
 Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
-Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: <b>6</b>
-Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
+Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: <b>7</b>
+Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
 Please note that each additional compute capability significantly increases your build time and binary size.
@@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.5.0rc1 on Linux:
+for TensorFlow 1.5.0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0rc1-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
@@ -461,8 +461,8 @@ Stack Overflow and specify the `tensorflow` tag.
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
 
-<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr>
-<tr><td>tensorflow_gpu-1.5.0-rc1</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr>
+<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.4.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>6</td><td>8</td></tr>
 <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
@@ -478,7 +478,7 @@ Stack Overflow and specify the `tensorflow` tag.
 **Mac**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.2.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
@@ -491,8 +491,8 @@ Stack Overflow and specify the `tensorflow` tag.
 **Windows**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
-<tr><td>tensorflow_gpu-1.5.0-rc1</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
+<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.4.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>6</td><td>8</td></tr>
 <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 8d0eb7966f..86a111c2ec 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -30,13 +30,13 @@ If you are installing TensorFlow with GPU support using one of the mechanisms
 described in this guide, then the following NVIDIA software must be
 installed on your system:
 
-  * CUDA® Toolkit 8.0. For details, see
+  * CUDA® Toolkit 9.0. For details, see
     [NVIDIA's
     documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/)
     Ensure that you append the relevant Cuda pathnames to the `%PATH%`
     environment variable as described in the NVIDIA documentation.
-  * The NVIDIA drivers associated with CUDA Toolkit 8.0.
-  * cuDNN v6.0. For details, see
+  * The NVIDIA drivers associated with CUDA Toolkit 9.0.
+  * cuDNN v7.0. For details, see
     [NVIDIA's documentation](https://developer.nvidia.com/cudnn).
     Note that cuDNN is typically installed in a different location from the
     other CUDA DLLs. Ensure that you add the directory where you installed
diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md
index 2b4896c381..9049a5a9f3 100644
--- a/tensorflow/docs_src/programmers_guide/graphs.md
+++ b/tensorflow/docs_src/programmers_guide/graphs.md
@@ -125,14 +125,14 @@ an operation:
   @{tf.Tensor} accepts an optional `name` argument. For example,
   `tf.constant(42.0, name="answer")` creates a new @{tf.Operation} named
   `"answer"` and returns a @{tf.Tensor} named `"answer:0"`. If the default graph
-  already contained an operation named `"answer"`, the TensorFlow would append
+  already contains an operation named `"answer"`, then TensorFlow would append
   `"_1"`, `"_2"`, and so on to the name, in order to make it unique.
 
 * The @{tf.name_scope} function makes it possible to add a **name scope** prefix
   to all operations created in a particular context. The current name scope
   prefix is a `"/"`-delimited list of the names of all active @{tf.name_scope}
   context managers. If a name scope has already been used in the current
-  context, TensorFlow appens `"_1"`, `"_2"`, and so on. For example:
+  context, TensorFlow appends `"_1"`, `"_2"`, and so on. For example:
 
   ```python
   c_0 = tf.constant(0, name="c")  # => operation named "c"