anchor.diff:  
   Despite all this, the cleaned raining data manages to improve baseline model score to ~0.55.
   
  +== Step3: Real work begins...
   
   
   
  
negative.code: My methodology is always to first start with a quick and simple baseline model and setup and end-end working code. In case of this challenge, this means:
  
  * Data related I/O - Loading, train/test splits, optional preprocessing blocks
  * Baseline model: I used FCN encoder-decoder as they learn end-end, simple and showed good results on other segmenatation problems. I also retrofitted it with batch normalization, ELU activation, and all that jazz.
  * Clean code: Try to organize code into classes. Having function blocks everywhere makes the code messy and error prone.
  * Submission code: Code to generate submission file.
  
  