Introduction to Machine Learning - 11 - Manifold learning and t-SNE
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- Опубліковано 21 тра 2024
- Lecture 11 in the Introduction to Machine Learning (aka Machine Learning I) course by Dmitry Kobak, Winter Term 2020/21 at the University of Tübingen.
Absolutely amazing video course. Especially after looking at other sources I notice how valuable this is. Every video achieves to combine the intuition and math in a concise was.
I recommend the videos to anyone who wants to learn about ML.
The best video on this topic I have found so far by a large margin. Excellent work!
Worth every second. You are a blessing to humanity.
Excellent talk with spot on visuals and explanations. Thanks!
Amazingly explained, It's such a great resource.
Beautiful explanations!
Amazing! Super interesting and understandable!
Great explanation with both details and good examples
Amazing Lecture, very well explained! Thank you for sharing!
Incredibly explained. Congratulations!
It is an amazing course, worth the time to watch and learn from it.
what an amazing explanations.......................well done............BRAVO!
Top quality lecture, thanks for sharing
So well explained! The best video resource I have seen on t-SNE so far!
Wonderful job. Really enjoy watching this.
23:20 perplexity - adjust sigma for each i so that we reach perplexity=30. may be small in dense group, but big in sparse group.
43:40 crowding problem
Fabulous video! This was really helpful, thank you!
Awesome explanations. Thank you very much.
Amazing course with great vizualisations ! thank you very much
Thank you for your awesome explanation and illustrations nive thank you very much
t-SNE is 1) non-linear 2) non-parametric (aka stochastic, non-deterministic) 3:28-4:20
8:46 MNIST
9:22 PCA's visual
17:14 17:57
18:45 t-SNE's visual
31:29❗2 separate blue clusters cannot get together
32:41 the fix: increase "Early Exaggeration" temporarily to increase the attraction force and then decrease back
Thank you for this course!
amazing content
Bravo! Thank you very much.
amazing lecture. Please post more videos.
Excellent lecture, thanks
Amazing!
Great lesson.
How can you use t-SNE not just for visualization but also for classification?
Does t-SNE take into account that some variables are more related with the formation of the cluster and other just add noise?
I mean, in some moedls you can calculate the p-value and the SHAP for each variable. Can you get this kind of information here?
Thanks a lot for greay content
Greetings from Spain
Respect!
Very good lesson
amazing, thx.
This video is the bees knees
how can one get good results with PCA init as don't we lose valuable non-linear information?
It's like your cup when u add the coffee powder into water
Where can we find lecture notes?
Use the subtitles/closed captions?