Document the grt123 algorithm

[reubano]

Overview

Participants in the Data Science Bowl produced several algorithms that we would like to incorporate. To help facilitate this effort, we also want to add documentation so that contributors can make an educated decision when selecting an algorithm to incorporate.

Expected Behavior

This documentation should enable people to:

• view the library dependencies and license
• understand its pros/cons
• evaluate its performance/accuracy
• identify which areas of the codebase to target for improvement

Design doc reference: Detect and select

Algorithm info

key	value
team	grt123
rank	1
repo	https://github.com/lfz/DSB2017
trained models	https://github.com/lfz/DSB2017/tree/master/model
converted branch	https://github.com/concept-to-clinic/DSB2017
ML engine	pytorch
engine-version	0.1.10+ac9245a
ML backend
backend-version
training method
architecture
algorithm
OS	Ubuntu
OS version	14.04
Python version	2.7
CUDA version	8
cuDNN version	5.1
notes

Technical details

• This feature should be implemented as a new markdown file in the docs folder

Acceptance criteria

• effective documentation for the above

NOTE: All PRs must follow the standard PR checklist.