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:
Background on Old-School Computer Vision:
Amazon EC2:
AMI NAME : TBA
AMI Username: TBA
AMI Password: TBA
Google Cloud :
GENERAL INSTRUCTIONS: TBA
- 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.
Amazon EC2:
AMI NAME : TBA
AMI Username: TBA
AMI Password: TBA
Google Cloud :
GENERAL INSTRUCTIONS: TBA
