I found myself going back and trying to find where things were so I created timeline from the point where I realized I needed one. I have videos downloaded so these are not links to youtube but timings.

**Video 2**

0:39:50 explanation of decomposition diagram in terms of document-words

0:40:35 NMF start

0:43:00 Applications of NMF

0:46:08 NMF in sklearn

0:48:00 TF-IDF

0:49:45 NMF in Summary

0:50:45 NMF from scratch in numpy, using SGD (gradient descent notebook walthrough)

0:56:50 Stochastic gradient descend

0:57:55 SGD Excel spreadsheet

1:02:00 Applying SGD to NMF

1:12:10 PyTorch

1:23:00 PyTorch: Autograd

1:35:00 Truncated SVD

1:36:15 Shortcomings of classical algorithms for decomposition

**Video 3**

0:00:30 Review matrix vector product

0:02:15 Review matrix matrix product

0:05:03 Jeremy Linear combinations data science perspective

0:06:30 Matrix multiplication

0:07:15 Four considerations for algorithms

0:09:50 Considerations parallelization

0:17:30 Return to NMF, SVD

0:18:45 Count matrix, TF-IDF excel

0:21:18 SVD

0:31:00 Block matrix

0:36:32 Perspective of SVD within excel

0:37:00 NMF

0:48:30 Review of notebook

0:50:45 Tweet Numpy

0:52:00 PyTorch revisit

0:55:15 Pytorch basics

1:00:15 Comparing Approaches in notebook 2

1:05:00 Randomized SVD

1:11:18 3 Blue 1 Brown video - Video 3 (Chapter 2)

1:21:33 End of 3B1B Video

1:22:40 Return to notebook

**Video 4**

0:00:15 What SVD is

0:01:07 Randomized SVD

0:03:45 Complexity of SVD

0:09:40 Why doing randomized SVD is ok

0:12:40 Implementing Randomized SVD

0:15:41 Randomized SVD exercise loop

0:23:20 johnson lindenstrauss lemma

0:24:42 Notebook 3 start

0:28:30 Picture of whole video

0:33:00 SVD

0:49:10 Background removal on Rank 1 matrix

0:54:00 PCA

0:57:25 Applications of Robust PCA

1:02:00 L1 induces sparsity

1:08:10 Robust PCA as Optimization problem

1:10:20 Implementing an algorithm from a paper

1:15:00 Details of algorithm