message: Update documentation to reflect new TFLite examples repo location
diff: This tutorial provides a simple Android mobile application to classify images
  using the Android device camera. In this tutorial, you will download the demo
  -application from the Tensorflow repository, build it on your computer, and
  -install it on your Android device. You will also learn how to customize the
  +application from the Tensorflow examples repository, build it on your computer,
  +and install it on your Android device. You will also learn how to customize the
  application to suit your requirements.
  ### Prerequisites
  @@ -16,17 +16,18 @@ application to suit your requirements.
  ### Step 1. Clone the TensorFlow source code
  -Clone the GitHub repository to your computer to get the demo application.
  +Clone the TensorFlow examples GitHub repository to your computer to get the demo
  +application.
  ```
  -git clone https://github.com/tensorflow/tensorflow
  +git clone https://github.com/tensorflow/examples
  ```
  Open the TensorFlow source code in Android Studio. To do this, open Android
  Studio and select `Open an existing project` setting the folder to
  -`tensorflow/lite/examples/android`
  +`examples/lite/examples/image_classification/android`
  <img src="images/classifydemo_img1.png" />
  @@ -79,16 +80,16 @@ Re-installing the app may require you to uninstall the previous installations.
  This mobile application gets the camera input using the functions defined in the
  file CameraActivity.java in the folder
  -`tensorflow/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraActivity.java.`
  +`examples/lite/examples/image_classification/android/app/src/main/java/org/tensorflow/lite/examples/classification/CameraActivity.java.`
  This file depends on `AndroidManifest.xml` in the folder
  -`tensorflow/tensorflow/lite/examples/android/app/src/main` to set the camera
  -orientation.
  +`examples/lite/examples/image_classification/android/app/src/main` to set the
  +camera orientation.
  ### Pre-process bitmap image
  The mobile application code that pre-processes the images and runs inference is
  in
  -`tensorflow/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java.`
  +`examples/lite/examples/image_classification/android/app/src/main/java/org/tensorflow/lite/examples/classification/tflite/Classifier.java.`
  Here, we take the input camera bitmap image and convert it to a Bytebuffer
  format for efficient processing. We pre-allocate the memory for ByteBuffer
  object based on the image dimensions because Bytebuffer objects can't infer the
  