Weekly Schedule
Basics
- Week
- Topic
- Refs
- Notes
- Week 1 Jan 20
- Logistics + Brief History on Deep Learning
- None
- slides
- Week 1 Jan 22
- Perceptrons Algorithm;
Intro to Multi-layer Perceptrons (MLPs) - DiDL (Ch. 4-5); tensorflow playground
- slides,
- Perceptrons Algorithm;
Training Neural Networks
- Week
- Topic
- References
- Notes
- Week 2 Jan 27
- MLPs (continue);
Backpropagation - DiDL (Ch. 12);
- slides
- MLPs (continue);
- Week 2 Jan 29
- Pytorch tutorial on Perceptron and MLPs;
- DiDL (Ch. 3.7, 5.6, 8.5)
- slides, Jupyter notebook
- Homework
Jan 29 - HW 1 Release
- HW 1.zip
- Week 3 Feb 3
- Different Gradient Optimizers; Learning rate scheduling
- DiDL (Ch. 12); Gradient Descent Demo
- slides
- Week 3 Feb 5
- Overfitting, Underfitting; Early Stopping
- DiDL (Ch. 3.7, 5.6, 8.5)
- slides
- Week 4 Feb 10
- L1/L2 Regularization, Dropouts
- DiDL (Ch. 3.7, 5.6, 8.5)
- slides
- Week 4
Feb 12 - PyTorch Tutorial for Gradient Optimizers, learning rates, and regularization
- DiDL (Ch. 3.7, 5.6, 8.5)
- slides
- Homework
Jan 29 - Quiz 1 Release
- Quiz.pdf
- Week 5
Feb 17
Natural Language Processing
- Week
- Topic
- References
- Notes
- Week 5
Feb 19 - Language modeling; Tokenization; N-gram;
- DiDL (Ch. 9.4-9.7)
- slides
- Week 6
Feb 24 - Skip gram model; CBOW model
- DiDL (Ch. 9.4-9.7)
- slides
- Week 6
Feb 26 - Recurrent Neural Networks (RNNs)
- DiDL (Ch. 9.4-9.7)
- slides
- Week 7
Mar 3 - Homework
Mar 3 - HW 2 Release
- HW 2
- Homework
- Week 7
Mar 3 - Pytorch tutorial on tokenization; text processing; Ngram
- None
- ipynb
- Week 7
Mar 5 - Pytorch tutorial on RNN, RNNLM
- None
- ipynb
- Week 8
Mar 10 - Back-Propagation Through Time (BPTT); Quiz 3
- DiDL (Ch. 11);
- quiz 3.pdf
- Week 8
Mar 12 - Gradient vanishing/explosing; RNN extension:
- DiDL (Ch. 11);
- slides
SPRING BREAK
- Week
- No Class
- Week 9
Mar 17 - No Class
- Week 9
Mar 19 - No Class