t-SNE Simply Explained

The t-SNE method in Data Science clearly and carefully explained!
0:00 Concept of Neighbors
6:25 Neighbor Similarity
8:17 Note on Standard Deviation
10:48 Moving to Lower Dimensions
22:38 KL Divergence
Resources:
Original t-SNE Paper : lvdmaaten.github.io/publicati...
KL Divergence Video : • The KL Divergence : Da...

[@SasukeBlackHeart94]

this guy's seriously underrated

[@SleekGreek]

Your channel is such a gem. I hope you get more attention, you deserve praise the likes of of 3B1B and orgo chem tutor or professor Leonard

[@ritvikmath]

Thanks! 😃

[@MrErick1160]

I don't think I've ever been explained something so well. I feel like a genius right now! thank you haha

[@jameslucas5590]

This guy is great. Wish I had you back in 2013. For your audience that wants to practice in R.
# Load required library for t-SNE
library(Rtsne)
# Generate a random dataset with 100 data points and dimensionality of 10
set.seed(123)
n

[@ritvikmath]

thanks!

[@priyaarora7169]

This channel is a gem, unfortunately found out so late. Could u also make a complete series on ML / DL / NN / Generative AI concepts with practical tutorials too?

[@simpleme989]

Extremely well done! thank you for sharing this.

[@Daily_language]

very clear explanation and help me understand t-sne. Great job! Subscribed your channel

[@hungchen6604]

Thanks for the great explanation, especially on why using Cauchy in the low-dim space!

[@ritvikmath]

Glad it was helpful!

[@emmanuellawal2694]

I'm just flooding my brain with new information, I believe I'll understand it indue time. Nice video as always

[@ritvikmath]

thanks!

[@felix1840]

Awesome as usual!

[@zakariaelkazdam7077]

Amazing explanation , thank you !!!

[@priyaarora7169]

Hi Ritvik, could u make a video for time-series data with explanation of different seasonalities (weekend, week-days, daily, hourly etc). Main ask is to understand how to analyse one data for seeing different seasonalities, and modeling it. Moreover, a video of applying different time-series models on the same?. Thank will be very useful for people like me who have just started learning time-series dataset. Thank you very much for your efforts, and time

[@woodworkingaspirations1720]

Beautiful lecture

[@avinashanand2163]

Amazing Content. Please make more videos, your videos are so easy to understand. Thanks a lot

[@ritvikmath]

Thank you!

[@includestdio.h8492]

Wow... thank u, u really the best

[@nibydlo]

This wasn't easy, but finally I got the idea. Thank you!

[@user-lo5tl5wf1s]

very good !!!

[@diabolo19x]

Super video! What about a video on the maths and intuition behind variational auto encoders ?❤

[@ritvikmath]

Great suggestion!

[@jaivratsingh9966]

super!

[@shashanksistla5400]

Hi Ritvik. I believe you've glanced over the fact that when normalizing the similarities in high-dimensional space, we use just the pairs with the point in question (i), but when normalizing the similarities in the low-dimensional space, we use all pairwise points.
What is the intuition behind this?

[@mdhvsnkr2657]

you dint over-explain believe me! XD. Overall superbly explained, thank you very much!

[@riteshdadlani353]

What a great fucking video

[@mabmab100]

its KŌ-SHE