Return GitHub links for models to tensorflow.org/code links

tensorflow/docs_src/tutorials/deep_cnn.md

@@ -83,21 +83,21 @@ for details.  It consists of 1,068,298 learnable parameters and requires about
 ## Code Organization
 
 The code for this tutorial resides in
-[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/).
+[`models/tutorials/image/cifar10/`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/).
 
 File | Purpose
 --- | ---
-[`cifar10_input.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format.
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
-[`cifar10_train.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
-[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
-[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
+[`cifar10_input.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format.
+[`cifar10.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
+[`cifar10_train.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
+[`cifar10_multi_gpu_train.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
+[`cifar10_eval.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
 
 
 ## CIFAR-10 Model
 
 The CIFAR-10 network is largely contained in
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10.py).
+[`cifar10.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10.py).
 The complete training
 graph contains roughly 765 operations. We find that we can make the code most
 reusable by constructing the graph with the following modules:

tensorflow/docs_src/tutorials/word2vec.md

@@ -23,7 +23,7 @@ straight in, feel free to look at the minimalistic implementation in
 This basic example contains the code needed to download some data, train on it a
 bit and visualize the result. Once you get comfortable with reading and running
 the basic version, you can graduate to
-[tensorflow_models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py)
+[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py)
 which is a more serious implementation that showcases some more advanced
 TensorFlow principles about how to efficiently use threads to move data into a
 text model, how to checkpoint during training, etc.
@@ -108,7 +108,7 @@ $$
 
 where \\(\text{score}(w_t, h)\\) computes the compatibility of word \\(w_t\\)
 with the context \\(h\\) (a dot product is commonly used). We train this model
-by maximizing its [log-likelihood](https://en.wikipedia.org/wiki/Likelihood_function) 
+by maximizing its [log-likelihood](https://en.wikipedia.org/wiki/Likelihood_function)
 on the training set, i.e. by maximizing
 
 $$
@@ -130,7 +130,7 @@ context \\(h\\), *at every training step*.
 
 On the other hand, for feature learning in word2vec we do not need a full
 probabilistic model. The CBOW and skip-gram models are instead trained using a
-binary classification objective ([logistic regression](https://en.wikipedia.org/wiki/Logistic_regression)) 
+binary classification objective ([logistic regression](https://en.wikipedia.org/wiki/Logistic_regression))
 to discriminate the real target words \\(w_t\\) from \\(k\\) imaginary (noise) words \\(\tilde w\\), in the
 same context. We illustrate this below for a CBOW model. For skip-gram the
 direction is simply inverted.
@@ -341,7 +341,7 @@ t-SNE.
 Et voila! As expected, words that are similar end up clustering nearby each
 other. For a more heavyweight implementation of word2vec that showcases more of
 the advanced features of TensorFlow, see the implementation in
-[tensorflow_models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py).
 
 ## Evaluating Embeddings: Analogical Reasoning
 
@@ -357,7 +357,7 @@ Download the dataset for this task from
 
 To see how we do this evaluation, have a look at the `build_eval_graph()` and
 `eval()` functions in
-[tensorflow_models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py).
 
 The choice of hyperparameters can strongly influence the accuracy on this task.
 To achieve state-of-the-art performance on this task requires training over a
@@ -385,13 +385,13 @@ your model is seriously bottlenecked on input data, you may want to implement a
 custom data reader for your problem, as described in
 @{$new_data_formats$New Data Formats}.  For the case of Skip-Gram
 modeling, we've actually already done this for you as an example in
-[tensorflow_models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py).
 
 If your model is no longer I/O bound but you want still more performance, you
 can take things further by writing your own TensorFlow Ops, as described in
 @{$adding_an_op$Adding a New Op}.  Again we've provided an
 example of this for the Skip-Gram case
-[tensorflow_models/tutorials/embedding/word2vec_optimized.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec_optimized.py).
+[models/tutorials/embedding/word2vec_optimized.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec_optimized.py).
 Feel free to benchmark these against each other to measure performance
 improvements at each stage.