Introduction to Machine Learning - 11 - Manifold learning and t-SNE 

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!

It is an amazing course, worth the time to watch and learn from it.

Excellent talk with spot on visuals and explanations. Thanks!

Worth every second. You are a blessing to humanity.

Amazingly explained, It's such a great resource.

what an amazing explanations.......................well done............BRAVO!

Amazing Lecture, very well explained! Thank you for sharing!

Incredibly explained. Congratulations!

Great explanation with both details and good examples

So well explained! The best video resource I have seen on t-SNE so far!

Beautiful explanations!

Amazing! Super interesting and understandable!

Wonderful job. Really enjoy watching this.

Top quality lecture, thanks for sharing

Fabulous video! This was really helpful, thank you!

Awesome explanations. Thank you very much.

Amazing course with great vizualisations ! thank you very much

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

Thanks a lot for greay content

Bravo! Thank you very much.

Thank you for this course!

Thank you for your awesome explanation and illustrations nive thank you very much

amazing content

Amazing!

Respect!

Excellent lecture, thanks

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?

Greetings from Spain

amazing, thx.

amazing lecture. Please post more videos.

This video is the bees knees

Very good lesson

how can one get good results with PCA init as don't we lose valuable non-linear information?

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

It's like your cup when u add the coffee powder into water

Where can we find lecture notes?