René's URL Explorer Experiment


Title: No title

X Title: Machine Learning @ VU

X Description: Course materials from the Machine Learning course of the Vrije Universiteit Amsterdam.

X: @pbloemesquire

direct link

Domain: dlvu.github.io

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twitter:imagehttps://www.vu.nl/nl/Images/VUlogo_NL_530px_tcm289-201740.png

Links:

1. Introduction https://dlvu.github.io/introduction
2. Backpropagation https://dlvu.github.io/backpropagation
3. Convolutions https://dlvu.github.io/cnns
4. Tools of the trade https://dlvu.github.io/tools
5. Sequences https://dlvu.github.io/sequences
6. Latent variable models https://dlvu.github.io/vae
8. Graphs https://dlvu.github.io/graphs
9. Self-attention https://dlvu.github.io/sa
11. Diffusion https://dlvu.github.io/diffusion
1.1 Neural networkshttps://dlvu.github.io/introduction/#video-000
1.2 Classification and regressionhttps://dlvu.github.io/introduction/#video-020
1.3 Autoencodershttps://dlvu.github.io/introduction/#video-034
playlisthttps://www.youtube.com/watch?v=MrZvXcwQJdg&list=PLIXJ-Sacf8u53w3iLVjYImNXcLAPV2Do_&ab_channel=DLVU
pdfhttps://dlvu.github.io/pdf/lecture01.introduction.annotated.pdf
2.1 Scalar backpropagationhttps://dlvu.github.io/backpropagation/#video-004
2.2 Tensor backpropagationhttps://dlvu.github.io/backpropagation/#video-042
2.3 Automatic differentiationhttps://dlvu.github.io/backpropagation/#video-066
2.4 Tensor details*https://dlvu.github.io/backpropagation/#video-042
playlisthttps://www.youtube.com/watch?v=idO5r5eWIrw&list=PLIXJ-Sacf8u7YQ77QmD5rFgAlDgFLqZ4b&index=4&ab_channel=DLVU
pdfhttps://dlvu.github.io/pdfs/lecture02.backpropagation.annotated.pdf
3.1 Introductionhttps://dlvu.github.io/cnns/#video-000
3.2 Conv1D (a)https://dlvu.github.io/cnns/#video-016
3.3 Conv1D (b)https://dlvu.github.io/cnns/#video-042
3.3 Conv2D, Conv3D, ConvNDhttps://dlvu.github.io/cnns/#video-060
playlisthttps://www.youtube.com/watch?v=rOuF5r5GduQ&list=PLIXJ-Sacf8u4koFI1FzdM6KYVDCLhaepZ&ab_channel=DLVU
pdfhttps://dlvu.github.io/pdfs/lecture03.cnns.annotated.pdf
4.1 Deep learning in practicehttps://dlvu.github.io/tools/#video-003
4.2 Why does any of this work at all?https://dlvu.github.io/tools/#video-044
4.3 Understanding optimizershttps://dlvu.github.io/tools/#video-066
4.4 The bag of trickshttps://dlvu.github.io/tools/#video-101
playlisthttps://www.youtube.com/playlist?list=PLIXJ-Sacf8u4XtBpteHSsW9j0WCx8MYbv
pdfhttps://dlvu.github.io/pdfs/lecture04.tools.annotated.pdf
5.1 Learning from sequenceshttps://dlvu.github.io/sequences/#video-002
5.2 Recurrent neural networkshttps://dlvu.github.io/sequences/#video-042
5.3 LSTMs and friendshttps://dlvu.github.io/sequences/#video-060
5.4 CNNs for sequential datahttps://dlvu.github.io/sequences/#video-084
5.5 ELMo, a case studyhttps://dlvu.github.io/sequences/#video-102
playlisthttps://www.youtube.com/playlist?list=PLIXJ-Sacf8u4koFI1FzdM6KYVDCLhaepZ
pdfhttps://dlvu.github.io/pdfs/lecture05.sequences.annotated.pdf
6.1 Why Generative Modelinghttps://dlvu.github.io/vae/#video-002
6.2 Autoencodershttps://dlvu.github.io/vae/#video-014
6.3 Variational Autoencodershttps://dlvu.github.io/vae/#video-023
pdfhttps://dlvu.github.io/pdfs/lecture06.latentvariablemodels.annotated.pdf
7.0 Introductionhttps://dlvu.github.io/repr/#video-000
7.1 VAE Implementationhttps://dlvu.github.io/repr/#video-008
7.2 KL Divergencehttps://dlvu.github.io/repr/#video-023
7.3 MMD-VAEhttps://dlvu.github.io/repr/#video-035
pdfhttps://dlvu.github.io/pdfs/lecture07.UnsupervisedRepresentation.unannotated.pdf
8.1 Introduction - Graphs (1A)https://dlvu.github.io/graphs/#video-002
8.2 Introduction - Embeddings (1B)https://dlvu.github.io/graphs/#video-017
8.3 Graph Embedding Techniqueshttps://dlvu.github.io/graphs/#video-025
8.4 Graph Neural Networkshttps://dlvu.github.io/graphs/#video-048
8.5 Query embeddinghttps://dlvu.github.io/graphs/#video-064
playlisthttps://www.youtube.com/playlist?list=PLIXJ-Sacf8u5IU-oyWn5bwF6c8XcR1TAR
pdfhttps://dlvu.github.io/pdfs/lecture08.graphs.annotated.pdf
9.1 Self-attentionhttps://dlvu.github.io/sa/#video-002
9.2 Transformershttps://dlvu.github.io/sa/#video-028
9.3 Famous transformershttps://dlvu.github.io/sa/#video-048
9.4 Scaling uphttps://dlvu.github.io/sa/#video-072
playlisthttps://www.youtube.com/playlist?list=PLIXJ-Sacf8u7UwAsGYFR1952pzQux5Lb1
pdfhttps://dlvu.github.io/pdfs/lecture09.self-attention.annotated.pdf
pdfhttps://dlvu.github.io/pdfs/lecture10.reinforcementlearning.pdf
11.1 Naive diffusionhttps://dlvu.github.io/diffusion/#video-005
11.2 Understanding Gaussianshttps://dlvu.github.io/diffusion/#video-022
11.3 Gaussian diffusionhttps://dlvu.github.io/diffusion/#video-049
playlisthttps://www.youtube.com/watch?v=mCVkLU2x4xY&list=PLIXJ-Sacf8u4Nq3vmR1Nde9UlKiPaecEA&pp=iAQB
pdfhttps://dlvu.github.io/pdfs/lecture11.diffusion.annotated.pdf
12.0 Introductionhttps://dlvu.github.io/generalization/#video-000
12.1 Reviewhttps://dlvu.github.io/generalization/#video-002
12.2 Problemhttps://dlvu.github.io/generalization/#video-017
12.3 Generalization Boundhttps://dlvu.github.io/generalization/#video-028
pdfhttps://dlvu.github.io/pdfs/lecture12.generalization.pdf
Ahttps://youtu.be/vTyZH8oqTec
Bhttps://youtu.be/i7-nhWSFsZ8
Chttps://youtu.be/uk3TGBQqMtU
Dhttps://youtu.be/I5lJ7Z-rL1A
pdfhttps://dlvu.github.io/slides/dlvu.lecture01.pdf
Ahttps://youtu.be/COhjLwjEpGM
Bhttps://youtu.be/7mTcWrnexkk
Chttps://youtu.be/dxZ8a-oIu7U
Dhttps://youtu.be/UpLtbV4L6PI
pdfhttps://dlvu.github.io/slides/dlvu.lecture02.pdf
Ahttps://youtu.be/rOuF5r5GduQ
Bhttps://youtu.be/VQqayqUCTwM
Chttps://youtu.be/Q7KekwUricc
Dhttps://youtu.be/2hS_54kgMHs
pdfhttps://dlvu.github.io/slides/dlvu.lecture03.pdf
Ahttps://youtu.be/rK20XfDN1N4
Bhttps://youtu.be/2JGlmBhQedk
Chttps://youtu.be/fbTCvvICk8M
Dhttps://www.youtube.com/watch?v=rT77lBfAZm4&ab_channel=DLVU
Ehttps://youtu.be/csAlW9HmwAQ
pdfhttps://dlvu.github.io/slides/dlvu.lecture05.pdf
Ahttps://youtu.be/EE5jTGP7wrM
Bhttps://youtu.be/ixI83iX7TV4
Chttps://youtu.be/uEvvs2YCxQk
Dhttps://youtu.be/mX92C0s0q1Y
pdfhttps://dlvu.github.io/slides/dlvu.lecture04.pdf
Ahttps://youtu.be/EfOZQvSCDsE
Bhttps://youtu.be/BTUehwU_5Uo
Chttps://youtu.be/ywNkaCdr6nA
pdfhttps://dlvu.github.io/slides/dlvu.lecture06.pdf
Ahttps://youtu.be/2nqtz3GzybQ
Bhttps://youtu.be/Ydk-GqUMQQM
pdfhttps://dlvu.github.io/slides/dlvu.lecture07.pdf
playlisthttps://www.youtube.com/playlist?list=PLIXJ-Sacf8u5IU-oyWn5bwF6c8XcR1TAR
pdfhttps://dlvu.github.io/slides/dlvu.lecture08.pdf
Ahttps://youtu.be/KmAISyVvE1Y
Bhttps://youtu.be/oUhGZMCTHtI
Chttps://youtu.be/MN__lSncZBs
pdfhttps://dlvu.github.io/slides/dlvu.lecture12.pdf
Ahttps://www.youtube.com/watch?v=t1I4NQTRXA0
Bhttps://www.youtube.com/watch?v=6KzJ1bpcNC4
Chttps://www.youtube.com/watch?v=PikByfX0p80
pdfhttps://dlvu.github.io/slides/dlvu.lecture09.pdf
Ahttps://www.youtube.com/watch?v=mCVkLU2x4xY
Bhttps://www.youtube.com/watch?v=ItI_gMuT5hw
Chttps://www.youtube.com/watch?v=zNCq1r4qI4Q
pdfhttps://dlvu.github.io/slides/dlvu.lecture11.pdf
Ahttps://youtu.be/_VPnu55UMCk
Bhttps://youtu.be/d_h6kY0s9yI
Chttps://youtu.be/Rhx6W3dGvK8
pdfhttps://dlvu.github.io/slides/dlvu.lecture10.pdf

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