Quick tutorial scrub

8 files changed, 8 insertions, 23 deletions

diff --git a/tensorflow/docs_src/tutorials/_toc.yaml b/tensorflow/docs_src/tutorials/_toc.yaml
index c4bf7260b1..1f539cdb49 100644
--- a/tensorflow/docs_src/tutorials/_toc.yaml
+++ b/tensorflow/docs_src/tutorials/_toc.yaml
@@ -49,7 +49,8 @@ toc:
   - title: Image recognition
     path: /tutorials/images/image_recognition
   - title: Image retraining
-    path: /tutorials/images/image_retraining
+    path: https://github.com/tensorflow/hub/tree/master/docs/tutorials/image_retraining.md
+    status: external
   - title: Convolutional Neural Networks
     path: /tutorials/images/deep_cnn
 
@@ -58,8 +59,6 @@ toc:
   section:
   - title: Recurrent Neural Networks
     path: /tutorials/sequences/recurrent
-  - title: Neural Machine Translation
-    path: /tutorials/sequences/seq2seq
   - title: Drawing classification
     path: /tutorials/sequences/recurrent_quickdraw
   - title: Simple audio recognition
diff --git a/tensorflow/docs_src/tutorials/images/deep_cnn.md b/tensorflow/docs_src/tutorials/images/deep_cnn.md
index 44a32d9d1d..43cca00d46 100644
--- a/tensorflow/docs_src/tutorials/images/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/images/deep_cnn.md
@@ -1,6 +1,6 @@
 # Convolutional Neural Networks
 
-> **NOTE:** This tutorial is intended for *advanced* users of TensorFlow
+Note: This tutorial is intended for *advanced* users of TensorFlow
 and assumes expertise and experience in machine learning.
 
 ## Overview
@@ -438,9 +438,6 @@ with a batch size of 64 and compare the training speed.
 
 ## Next Steps
 
-[Congratulations!](https://www.youtube.com/watch?v=9bZkp7q19f0) You have
-completed the CIFAR-10 tutorial.
-
 If you are now interested in developing and training your own image
 classification system, we recommend forking this tutorial and replacing
 components to address your image classification problem.
diff --git a/tensorflow/docs_src/tutorials/images/image_recognition.md b/tensorflow/docs_src/tutorials/images/image_recognition.md
index 332bcf54f0..432d470d0c 100644
--- a/tensorflow/docs_src/tutorials/images/image_recognition.md
+++ b/tensorflow/docs_src/tutorials/images/image_recognition.md
@@ -434,7 +434,6 @@ should be able to transfer some of that understanding to solving related
 problems.  One way to perform transfer learning is to remove the final
 classification layer of the network and extract
 the [next-to-last layer of the CNN](https://arxiv.org/abs/1310.1531), in this case a 2048 dimensional vector.
-There's a guide to doing this @{$image_retraining$in the how-to section}.
 
 
 ## Resources for Learning More
diff --git a/tensorflow/docs_src/tutorials/images/image_retraining.md b/tensorflow/docs_src/tutorials/images/image_retraining.md
deleted file mode 100644
index 27784eef9c..0000000000
--- a/tensorflow/docs_src/tutorials/images/image_retraining.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# How to Retrain Inception's Final Layer for New Categories
-
-**NOTE: This tutorial has moved to**
-https://github.com/tensorflow/hub/tree/master/docs/tutorials/image_retraining.md
diff --git a/tensorflow/docs_src/tutorials/non-ml/pdes.md b/tensorflow/docs_src/tutorials/non-ml/pdes.md
index 425e8d7084..b5a0fa834a 100644
--- a/tensorflow/docs_src/tutorials/non-ml/pdes.md
+++ b/tensorflow/docs_src/tutorials/non-ml/pdes.md
@@ -135,7 +135,6 @@ for i in range(1000):
   DisplayArray(U.eval(), rng=[-0.1, 0.1])
 ```
 
-![jpeg](../images/pde_output_2.jpg)
+![jpeg](../../images/pde_output_2.jpg)
 
 Look! Ripples!
-
diff --git a/tensorflow/docs_src/tutorials/sequences/recurrent.md b/tensorflow/docs_src/tutorials/sequences/recurrent.md
index 14da2c8785..715cc7856a 100644
--- a/tensorflow/docs_src/tutorials/sequences/recurrent.md
+++ b/tensorflow/docs_src/tutorials/sequences/recurrent.md
@@ -2,8 +2,8 @@
 
 ## Introduction
 
-Take a look at [this great article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
-for an introduction to recurrent neural networks and LSTMs in particular.
+See [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/){:.external}
+for an introduction to recurrent neural networks and LSTMs.
 
 ## Language Modeling
 
diff --git a/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md b/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md
index 1afd861738..37bce5b76d 100644
--- a/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md
+++ b/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md
@@ -13,7 +13,7 @@ In this tutorial we'll show how to build an RNN-based recognizer for this
 problem. The model will use a combination of convolutional layers, LSTM layers,
 and a softmax output layer to classify the drawings:
 
-<center> ![RNN model structure](../images/quickdraw_model.png) </center>
+<center> ![RNN model structure](../../images/quickdraw_model.png) </center>
 
 The figure above shows the structure of the model that we will build in this
 tutorial. The input is a drawing that is encoded as a sequence of strokes of
@@ -208,7 +208,7 @@ This data is then reformatted into a tensor of shape `[num_training_samples,
 max_length, 3]`. Then we determine the bounding box of the original drawing in
 screen coordinates and normalize the size such that the drawing has unit height.
 
-<center> ![Size normalization](../images/quickdraw_sizenormalization.png) </center>
+<center> ![Size normalization](../../images/quickdraw_sizenormalization.png) </center>
 
 Finally, we compute the differences between consecutive points and store these
 as a `VarLenFeature` in a
diff --git a/tensorflow/docs_src/tutorials/sequences/seq2seq.md b/tensorflow/docs_src/tutorials/sequences/seq2seq.md
deleted file mode 100644
index 8928ba4f7d..0000000000
--- a/tensorflow/docs_src/tutorials/sequences/seq2seq.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Sequence-to-Sequence Models
-
-Please check out the
-[tensorflow neural machine translation tutorial](https://github.com/tensorflow/nmt)
-for building sequence-to-sequence models with the latest Tensorflow API.