Deep Learning for Computer Vision
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  • Syllabus
  • Assignments And Resources
  • Instructor and TAs
  • Home
  • Syllabus
  • Assignments And Resources
  • Instructor and TAs
Assignments and Final Project:
  • Assignment 1: Bayes Theorem, Minimum Error Rate Classifier, Linear SVMs (jupyter notebook)
  • Assignment 2: Multi-Layer Perceptron (jupyter notebook)
  • Assignment 3: A Deeper Multi-Class MLP with Regularization (jupyter notebook)
  • Assignment 4: Getting Deeper with Digits: LeNet (jupyter notebook)
  • Project Proposal (jupyter notebook)
  • Assignment 5: Domain Transfer and Fine-Tuning (jupyter notebook)
  • Project Report and Presentation (schedule for presentations)
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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