fixed errors in README and updated README with Beginner Tutorial Information

Author: Thomas Mulc

README.md

@@ -4,27 +4,36 @@ Currently, there are few examples of distributed TensorFlow code.  Further, the
 
 This is collection of examples for help getting started with distributed computing in TensorFlow and that can act as boilerplate code.  Many of the examples focus on implementing well-known distributed training schemes, such as those available in [Distriubted Keras](https://github.com/cerndb/dist-keras) which were discussed in the author's [blog post](http://joerihermans.com/ramblings/distributed-deep-learning-part-1-an-introduction/).  The official Distributed TensorFlow guide can be found [here]( https://www.tensorflow.org/deploy/distributed).  
 
-Almost all the examples can be run on a single machine with a CPU. 
+Almost all the examples can be run on a single machine with a CPU.
 
+## Beginner Tutorial
 
-## Examples
+See the Beginner Tutorial folder for notebooks demonstrating core concepts used in distributed TensorFlow.  The rest of the examples assume understanding of the beginner tutorial.
+
+* `Servers.ipynb` -- basics of TensorFlow servers
+* `Parameter Sever.ipynb` -- everything about parameter servers
+* `Local then Global Variable.ipynb` -- creates a graph locally then make global copies of the variables.  Useful for graphs that do local updates before pushing global updates (e.g. DOWNPOUR, ADAG, etc.)
+
+
+## Training Examples
 
 The complete list of examples is below.  The asynchronous examples are *easier* than the synchronous, so people getting started should first have a complete understanding of those before moving to synchronous examples.  The first example, `Non-Distributed Setup`, shows the basic learning problem we want to solve distributively; this example should be familiar to all since it doesn't use any distributed code.  The second example, `Distributed Setup` shows the same problem being solved with distributed code (i.e. with one parameter server and one worker). 
 
 * `Non-Distributed Setup`
 * `Distributed Setup`
 * `HogWild` (Asychronous SGD)
+* `DOWNPOUR` **TODO**
 * `ADAG` (Asynchronous Distributed Adaptive Gradients) **WIP**
 * `Synchronous SGD`
 * `Synchronous SGD different learning rates`
 * `SDAG` (Synchronous Distributed Adaptive Gradients) **WIP**
 * `Multiple GPUs Single Machine`
-* Dynamic SGD **(TODO)**
-* Asynchronous Elastic Averaging SGD (AEASGD) **(TODO)**
-* Asynchronous Elastic Averaging Momentum SGD (AEAMSGD) **(TODO)**
+* Dynamic SGD **TODO**
+* Asynchronous Elastic Averaging SGD (AEASGD) **TODO**
+* Asynchronous Elastic Averaging Momentum SGD (AEAMSGD) **TODO**
 
-## Running Examples
-All the examples (except the non-distributed example) live in a folder.  To run them, move to the example directory and run the bash script.
+## Running Training Examples
+All the training examples (except the non-distributed example) live in a folder.  To run them, move to the example directory and run the bash script.
 
 ```bash
 cd <example_name>/
@@ -46,18 +55,18 @@ sudo pkill python
 ## Links
 * [Official Documenation](https://www.tensorflow.org/deploy/distributed)
 * [Threads and Queues](https://www.tensorflow.org/programmers_guide/threading_and_queues)
-* [More TensorFlow Documentation](https://www.tensorflow.org/api_guides/python/train#Distributed execution)
+* [More TensorFlow Documentation](https://www.tensorflow.org/api_guides/python/train#Distributedexecution)
 
 ## Glossary
-* [Server](https://www.tensorflow.org/api_docs/python/tf/train/Server) -- encapsulates a Session and belongs to a cluster
+* [Server](https://www.tensorflow.org/api_docs/python/tf/train/Server) -- encapsulates a Session target and belongs to a cluster
 * [Coordinator](https://www.tensorflow.org/api_docs/python/tf/train/Coordinator) -- coordinates threads
 * [Session Manager](https://www.tensorflow.org/api_docs/python/tf/train/SessionManager) -- restores session and initialized variables and coordinates threads
 * [Supervisor](https://www.tensorflow.org/api_docs/python/tf/train/Supervisor) -- good for threads. Coordinater, Saver, and Session Manager. > Session Manager
 * [Session Creator](https://www.tensorflow.org/api_docs/python/tf/train/SessionCreator) -- Factory for creating a session?
 * [Monitored Session](https://www.tensorflow.org/api_docs/python/tf/train/MonitoredSession) -- Session.  initialization, hooks, recovery.
 * [Monitored Training Session](https://www.tensorflow.org/api_docs/python/tf/train/MonitoredTrainingSession) -- only distributed solution for sync optimization
 * [Sync Replicas](https://www.tensorflow.org/api_docs/python/tf/train/SyncReplicasOptimizer) -- wrapper of optimizer for synchronous optimization
-* [Scaffold](https://www.tensorflow.org/api_docs/python/tf/train/Scaffold) -- holds lots of meta training settings and passed to session creator
+* [Scaffold](https://www.tensorflow.org/api_docs/python/tf/train/Scaffold) -- holds lots of meta training settings and passed to Session creator
 
 ### Hooks
 * [Stop Hook](https://www.tensorflow.org/api_docs/python/tf/train/StopAtStepHook) -- Hook to request  stop training