Advanced Topics in Deep Learning
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  • Home
  • Reading List
  • Final Project Schedule
  • Project and Resources

Project Report

 You should structure your project report like the research papers we have been reading and presenting in class. 

Project Report:
  1. introduction
  2. related work
  3. description of what you did
  4. results and analysis
  5. conclusion
  6. short bibliography
  7. (OPTIONAL) appendix which includes supplemental material + your code.  please highlight the code that is yours.

Please take the report seriously.  It is hard to give credit for all your hard work, if the work is not clearly described in the report.  In writing the report, make clear what your contributions were beyond what is available on the internet.  If you re-implemented a deep learning model from the literature, describe this. If you built your project from an existing implementation,  describe this. It's optional to include the code you used for your project.

Resources

Software Resources (all one Google search away...):
  • Python
  • Jupyter Notebook
  • Keras
  • Tensorflow
  • Tensorboard
  • TFSlim
  • Theano
  • Caffe
  • TFLearn
Background on Deep Learning:
  • Deep Learning, Goodfellow, Bengio, and Courville
  • Stanford Deep Learning Course (CS231n)
  • Understanding LSTM Networks, Colah
  • Yes, You Should Understand Backprop, Karpathy
  • Tensorflow Tutorial
  • Keras

Background on Old-School Computer Vision:
  • Computer Vision: A Modern Approach, Forsyth and Ponce
  • Computer Vision: Algorithms and Applications, Richard Szeliski
  • Receptive Fields, Binocular Interaction, and Functional Architecture in the Cat's Visual Cortex, Hubel and Wiesel, 1962
  • Hubel and Weisel, Cat Experiments Video
  • MIT Summer Vision Project, Papert, 1966
  • ​Vision, Marr, 19
  • Computer Vision, Ballard and Brown, 1982
  • Robot Vision, Horn, 1985
  • Pattern Classification, Duda, Hart, and Stork​
  • Pegasos: Primal Estimated sub-Gradient Solver for SVM, Shalev-Shwartz et al.​​
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