Add docs/conditional_computation.md which was accidentally left out of an
earlier commit.

* Updated stats computation - fixes issues with validation stats

* Clarification of output (docs)

* Update

* Moved validation stats to separate function

* Fixed validation stats and added new summary stats

* Trimmed some comments.

* Improved figure for documentation

* Minor updates

Latest versions of Jupyter notebooks have a different syntax for
launching the server such that it listens on oll network interfaces
(this is useful if you are running the Jupyter server on one machine,
and connect to it from a browser on a different machine).

So:
	jupyter-notebook --ip=* --no-browser

is replaced by:
	jupyter-notebook --ip=0.0.0.0 --no-browser

* A temporary fix for issue 36

The thinning code assumes that the sgraph it is using
is not data-parallel, because it (currently) accesses the
layer-name keys using a "normalized" name ("module." is removed).

The bug is that in thinning.py#L73 we create a data_parallel=True
model; and then give it to sgraph.
But in other places thinning code uses "normalized" keys.  For
example in thinning.py#L264.

The temporary fix configures data_parallel=False in thinning.py#L73.

A long term solution should have SummaryGraph know how to handle
both parallel and not-parallel models.  This can be done by having
SummaryGraph convert layer-names it receives in the API to
data_parallel=False using normalize_layer_name.  When returning
results, use the de-normalized format.

* Fix the documentation error from issue 36
* Move some logs to debug and show in logging.conf how to enable DEBUG logs.

* Implemented as a Policy
* Integrated in image classification sample
* Updated docs and README

Enabling Early Exit strategy in image classifier example

* Adding PACT quantization method
* Move logic modifying the optimizer due to changes the quantizer makes into the Quantizer itself
* Updated documentation and tests

* Add Quantizer unit tests
* Require 'bits_overrides' to be OrderedDict to support overlapping
  patterns in a predictable manner + update documentation to reflect this
* Quantizer class cleanup
  * Use "public" nn.Module APIs instead of protected attributes
  * Call the builtins set/get/delattr instead of the class special methods
    (__***__)
  * Fix issues reported in #24
* Bug in RangeLinearQuantParamLayerWrapper - add explicit override of
  pre_quantized_forward accpeting single input (#15)
* Add DoReFa test to full_flow_tests

* Scale of bias and parentheses were wrong

Reviewed and looking good.  We have to set a convention for naming files.

Two places in the documentation gave the wrong path to the example
Alexnet sensitivity pruning schedule.