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Big Data: Term Project Final Submission

CS535
Spring 2020

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Final submission: Report and Software (Due on 5/1 by 5:00PM)

 

Final report (24 points), software (2 points)

Components of the final report
* Please make sure that you have addressed all the comments on your proposal.

1. Title of your project

This should be concise and self-descriptive.

2. Problem formulation

The final report should clearly identify the problem. It should include at least one or two carefully crafted paragraph that states and highlights the problem. The problem formulation should be able to answer following questions:

  • What is the problem you are solving?   This should also include the background for the problem.
  • Why is it interesting as a Big Data problem and who would use it if it were solved?

3. Your Methodology to solve the problem  

Describe your approach(s) to solve the problem and explain why you and your team made that choice. The description of the strategy should include, 

  • The algorithms/techniques/models you uses in this project.
    • Your data pre-processing and explorative analytics
    • Your Deep Learning model application
    • Othter models and methodologies, if applicable
  • The framework you uses in this project
  • The dataset ysou used in this project

NOTE: Your computation requirement for this semester is "a Distributed Deep Learning". As a part of your term project, you and your team are required to perform a deep learning over a distributed computing framework. Regarding the framework, you may use PyTorch, Horovod, TensorFlow with Spark. However, we will provide a tutorial video clip for using the distributed PyTorch. We have noticed that PyTorch works well in our cluster.

Please note that your problem may not be solved with applying a deep leearning model only. Please feel free to combine with other algorrithms if neeeded.

4. Results and Evaluation 

The report should include results of your project and evaluation of your software including metrics that you used to identify if you have succeeded or not.  If you came up with a metric, also provide an intuitive feel for what this metric captures and why this is appropriate.

For example, if your project involves classification, you can list accuracy measures that will be used and provide justification. Also, you should provide what your targe accuracy with your project.

You mush provide an analysis and/or justification of the evaluation and results.

 

5. Contributions

You should provide a table with a list of contributionf for each team members.

 

6. Bibliography

Included a bibliography.  All references must be cited in the report. 

  • The authors' names
  • The titles of the works
  • The names of publisher
  • The date (or year) the copies were published
  • The page numbers of your sources (if available)

9. Submission

If it is a team submission, please submit only one copy of and specify the team members in the author list associated with the document.

This document should be 3,000 ~ 4,000 words. Do not exceed the limit.

 

 

* Software (2 points)

Your team should provide a demonstration of your software. Please submit your source code with your final report via canvas. Do not submit any test data.

 

 

 


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