Latent Space Visualisation: PCA, t-SNE, UMAP | Deep Learning Animated

In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are especially useful when you want to visualise the latent space of an autoencoder.

If you want to learn more about these techniques, here are some key papers:

And if you want to learn about even more recent techniques such as TriMAP and PACMAP, here are the papers:

Chapters:
00:36 PCA
05:15 t-SNE
13:30 UMAP
18:02 Conclusion

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#DeepLearning #PCA #ArtificialIntelligence #tsne #DataScience #LatentSpace #Manim #Tutorial #machinelearning #education #somepi

[thorvaldspear]

Wow that mammoth 2D visualization using UMAP looked like it was opened up and flattened, you could tell it was a living thing of some sort. Incredible!

[Ouuiea]

My only comment to the video is that PCA real advantage is not speed, is interpretability. It's easy to read a principal component in terms of how it correlates with the original variables. Something you cannot do with t-SNE or UMAP. The video is an excellent work!

[SudhanvaDixit]

Prediction - A channel that's going to explode.

Watched multiple videos. Very crisp clear explanation with good animation. Thank you :)

[sharjeel_mazhar]

I must say, the way you teach is just brilliant! Those visualizations and all, i mean even a 10yo could understand it if he focuses just a lil bit! Can't wait till you reach the level of teaching us the Transformer models!

[robotics_hub]

Amazing visualization for a very difficult topic grasp. Many thanks!

[stringtheory]

Absolutely clear and crisp visualization of PCA!!

[Grenoble]

thank you for the insight without the fuss. I am a UMAP user and I am glad about your conclusion. Suscribed!

[ripequetzalcoatyl_]

This was so great please keep making videos brother you will reach great heights 🙌

[jchealyify]

A really solid explanation. Well done! You are a wonderful communicator and your visualizations are top notch.

I do have one very small suggestion that might help. When sweeping through hyperparameters and showing their effect on the embedding it can be helpful to correct a bit of the stochastic nature of layout. When transitioning between your embeddings in low dimensions it can be helpful to a user for you to run a procrustes algorithm on the two embeddings. This will just flip, rotate and scale the point clouds to be best aligned. It really helps users see consistent patterns as hyperparameters change without altering the embedding in any meaningful ways.

Keep up the fantastic work. I'll definitely be following your channel.

[ganpangyen]

Incredible visualization and simplification on the topic, especially with UMAP! The superiority of UMAP over t-SNE in terms of the final results and its lower sensitivity to hyperparameters really shows the power of math

[nitroseeks]

You are amazing. The visualisations in your lectures are top notch

[kagan]

Awesome video! I was hoping for a bit more of a friendly, intuitive explanation of the equations in t-SNE. Instead of just jumping into the equations, it would be great to get a sense of why they work the way they do and how they fit into the whole picture.

[andrer.]

I forgot about t-SNE. In my own research I have been using UMAP. But, I haven't heard of TriMAP and PaCMAP before. I am going to dive in deeper!

[aadimator]

Amazing visualization, pace, and aesthetics. Looking forward to seeing more from you. Best of luck 🤞

[williamz]

Wow, super cool! Love the visualizations! Very informative, much better than the PowerPoint presentations out there lol

[alin]

Excelent and clear animations, graphs and explanation, keep it on!

[laotzunami]

Your videos are such high quality! Thank you so much for putting this effort into them. I do data visualization, and I would love to start including more advanced machine learning models in what I make. I got a lot out of this video. I can't wait to see what is next :)

[rdkoala]

Killing it man, loving these videos, I'm so glad I found your channel!

[TheHHadouKen]

Hey I’ve been looking for a visual representation of feature selection en dimensionality reduction and your video is just amazing. The tone and animation make me thing about 3b1b, and that’s a compliment ! 😊

[virgenalosveinte]

Your channel is astounding brobro thank you