Lectures

I would like to thank Prof. Alexander Schwing, Prof. Aigou Han, Prof. Farzad Kamalabadi, and Prof. Corey Synder for their support and lecture documents which I’ve based much of my own work on.

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# Title Date Slides(HTML) Slides(Code) Recording
0 Lecture 0 - PyTorch Intro, tensors, views, indexing Jan 21 Lecture 0 slides Lecture 0 code Lecture 0 recording
1 Lecture 1 - PyTorch storage, indexing, cpu/gpu data types, functions Jan 23 Lecture 1 slides Lecture 1 code Lecture 1 recording
2 Lecture 2 - Linear algebra I Jan 28 Lecture 2 slides Lecture 2 code Lecture 2 recording
3 Lecture 3 - Linear algebra II & Matrix algebra Jan 30 Lecture 3 slides Lecture 3 code Lecture 3 recording
4 Lecture 4 - Matrix calculus II and computation graphs Feb 04 Lecture 4 slides Lecture 4 code Lecture 4 recording
5 Lecture 5 - Gradient descent Feb 06 Lecture 5 slides Lecture 5 code Lecture 5 recording
6 Lecture 6 - Primal optimization Feb 11 Lecture 6 slides Lecture 6 code Lecture 6 recording
7 Lecture 7 - Linear regression I Feb 13 Lecture 7 slides Lecture 7 code Lecture 7 recording
8 Lecture 8 - Linear regression II Feb 18 Lecture 8 slides Lecture 8 code Lecture 8 recording
9 Lecture 9 - Logistic regression Feb 20 Lecture 9 slides Lecture 9 code Lecture 9 recording
10 Lecture 10 - Multi-class logistic regression Feb 25 Lecture 10 slides Lecture 10 code Lecture 10 recording
11 Lecture 11 - PyTorch optimizers, datasets, dataloaders I Feb 27 Lecture 11 slides Lecture 11 code Lecture 11 recording
12 Lecture 12 - PyTorch optimizers, datasets, dataloaders II Mar 04 Lecture 12 slides Lecture 12 code Lecture 12 recording
13 Lecture 13 - Review for MT1 Mar 06 Lecture 13 slides Lecture 13 code Lecture 13 recording
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