Lectures
I would like to thank Prof. Alexander Schwing, Prof. Aigou Han, Prof. Farzas Kamalabadi, and Prof. Corey Synder for their support and lecture documents which I’ve based much of my own work on.
Also, quick hint, icons are links to slides and video contents!
| # | Title | Date | Slides | Recording |
|---|---|---|---|---|
| 0 | Lecture 0 - PyTorch Intro, tensors, views, indexing | Jan 21 | ||
| 1 | Lecture 1 - PyTorch storage, indexing, cpu/gpu data types, functions | Jan 23 | ||
| 2 | Lecture 2 - Linear algebra I | Jan 28 | ||
| 3 | Lecture 3 - Linear algebra II | Jan 30 | ||
| 4 | Lecture 4 - Auto-differentiation I | Feb 04 | ||
| 5 | Lecture 5 - Auto-differentiation II | Feb 06 | ||
| 6 | Lecture 6 - Primal optimization | Feb 11 | ||
| 7 | Lecture 7 - Linear regression I | Feb 13 | ||
| 8 | Lecture 8 - Linear regression II | Feb 18 | ||
| 9 | Lecture 9 - Logistic regression | Feb 20 | ||
| 10 | Lecture 10 - Multi-class logistic regression | Feb 25 | ||
| 11 | Lecture 11 - PyTorch optimizers, datasets, dataloaders I | Feb 27 | ||
| 12 | Lecture 12 - PyTorch optimizers, datasets, dataloaders II | Mar 04 | ||
| 13 | Lecture 13 - Review for MT1 | Mar 06 | ||
| 14 | Lecture 14 - Deep Nets I - Simple, multi-layer networks | Mar 25 | ||
| 15 | Lecture 15 - Deep Nets II - Convolutional networks | Mar 27 | ||
| 16 | Lecture 16 - Deep Nets III - Recurrent networks | Apr 01 | ||
| 17 | Lecture 17 - Principal component analysis | Apr 03 | ||
| 18 | Lecture 18 - K-means clustering, gaussian mixture models | Apr 08 | ||
| 19 | Lecture 19 - Generative adversarial networks | Apr 10 | ||
| 20 | Lecture 20 - Object detection, semantic segmentation | Apr 15 | ||
| 21 | Lecture 21 - Words and attention | Apr 17 | ||
| 22 | Lecture 22 - Transformer models | Apr 22 | ||
| 23 | Lecture 23 - Large language models I - architectures and training | Apr 24 | ||
| 24 | Lecture 24 - Large language models II - fine-tuning methods | Apr 29 | ||
| 25 | Lecture 25 - Course recap and celebration | May 06 |