NCF scripts with Distiller integration

This NCF implementation is based on the implementation found in the MLPerf
Training GitHub repository, specifically on the last revision of the code
before the switch to the extended dataset. See:
https://github.com/mlperf/training/tree/fe17e837ed12974d15c86d5173fe8f2c188434d5/recommendation/pytorch

We've made several modifications to the code:
* Removed all MLPerf specific code including logging
* In ncf.py:
  * Added calls to Distiller compression APIs
  * Added progress indication in training and evaluation flows
* In neumf.py:
  * Added option to split final FC layer
  * Replaced all functional calls with modules so they can be detected
    by Distiller
* In dataset.py:
  * Speed up data loading - On first data will is loaded from CSVs and
    then pickled. On subsequent runs the pickle is loaded. This is much
    faster than the original implementation, but still very slow.
  * Added progress indication during data load process
* Removed some irrelevant content from README.md