- >> HW 1: Bayes Theorem, Min Error Rate Classifier, Application for Computer Vision (jupyter notebook): Due: September 26, 2023. <<
- >> HW 2: Multi-Layer Perceptron: (jupyter notebook): Due: October 12, 2023. <<
- >> HW 3: A Deeper Multi-Class MLP (jupyter notebook): Due Oct 24, 2023. <<
- >> HW 4: Getting Deeper with Digits: LeNet (jupyter notebook): Due Nov 2, 2023. <<
- >> HW 5: Domain Transfer and Fine-Tuning (jupyter notebook): Due Nov 9, 2023. <<
- >> Project Proposal (jupyter notebook): Due Nov 7, 2023. <<
- Project Report and Presentation (schedule for presentations): See slack.
Software Resources (all one Google search away...):
- Python
- Jupyter Notebook
- Pytorch
- Tensorflow
Background on Deep Learning:
Background on Old-School Computer Vision:
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.
Google Cloud :
GENERAL INSTRUCTIONS: TBA