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This closes issues #280 and #65 (reopened).

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

Thanks to Dan Alistarh for bringing this issue to my attention.
The activations of Linear layers have shape (batch_size, output_size) and those
of Convolution layers have shape (batch_size, num_channels, width, height) and
this distinction in shape was not correctly handled.

This commit also fixes sparsity computation for very large activations, as seen
in VGG16, which leads to memory exhaustion.  One solution is to use smaller
batch sizes, but this commit uses a different solution, which counts zeros “manually”,
and using less space.

Also in this commit:
- Added a “caveats” section to the documentation.
- Added more tests.

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