* Added scale factor approximation in post-training quantization using
  integer multiply + shift. # of bits for integer multiplier is user
  configurable
* Updated documentation
* Updated post-train quant command line examples readme file

* Some refactoring to enable multiple clipping methods
* BREAKING: clip_acts as a boolean flag (either in command line
  or in function signature) will fail. Error message with valid
  values from is displayed.
* Implemented clipping activations at mean + N * std
  (N is user configurable)
* Additional tests
* Updated docs

Add finer control over the pruning logic, to accommodate more pruning
use-cases.
The full description of the new logic is available in the updated [documentation
of the CompressionScheduler](https://nervanasystems.github.io/distiller/schedule.html#pruning-fine-control), which is also part of this PR.

In this PR:

* Added a new callback to the CompressionScheduler:
compression_scheduler.before_parameter_optimization which is invoked
after the gradients are are computed, but before the weights are updated
by the optimizer.

* We provide an option to mask the gradients, before the weights are updated by the optimizer. 
We register to the parameter backward hook in order to mask the gradients.
This gives us finer control over the parameter updates.

* Added several DropFilter schedules.
DropFilter is a method to regularize networks, and it can also be
used to "prepare" a network for permanent filter pruning.

*Added documentation of pruning fine-control

* Bias handling:
  * Add 'bits_bias' parameter to explicitly specify # of bits for bias,
    similar to weights and activations.
  * BREAKING: Remove the now redundant 'quantize_bias' boolean parameter
* Custom overrides:
  * Expand the semantics of the overrides dict to allow overriding of
    other parameters in addition to bit-widths
  * Functions registered in the quantizer's 'replacement_factory' can
    define keyword arguments. Non bit-width entries in the overrides
    dict will be checked against the function signature and passed
  * BREAKING:
    * Changed the name of 'bits_overrides' to simply 'overrides'
    * Bit-width overrides must now be defined using the full parameter
      names - 'bits_activations/weights/bias' instead of the short-hands
      'acts' and 'wts' which were used so far.
  * Added/updated relevant tests
  * Modified all quantization YAMLs under 'examples' to reflect 
    these changes
  * Updated docs

Summary of changes:
(1) Post-train quantization based on pre-collected statistics
(2) Quantized concat, element-wise addition / multiplication and embeddings
(3) Move post-train quantization command line args out of sample code
(4) Configure post-train quantization from YAML for more fine-grained control

(See PR #136 for more detailed changes descriptions)

* Asymmetric post-training quantization (only symmetric supported so until now)
* Quantization aware training for range-based (min-max) symmetric and asymmetric quantization
* Per-channel quantization support in both training and post-training
* Added tests and examples
* Updated documentation

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

* 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