t-SNE Simply Explained

[@sherz1937]

BRO. This is an absolute gem. I've been bouncing from left to right looking for an easy glimpse into t-SNE. This helped me so much! Thank you

[@SasukeBlackHeart94]

this guy's seriously underrated

[@MrErick1160]

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

[@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! 😃

[@ravindrasonawane1997]

Wonderful. You have explained so complicated algorithm so elegantly. Thanks a lot!!

[@AlonKedem1000]

Best video content creator ever. I love your explanations!

[@ritvikmath]

Thanks! I'm glad you like them.

[@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!

[@hungchen6604]

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

[@ritvikmath]

Glad it was helpful!

[@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?

[@emmanuellawal2694]

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

[@ritvikmath]

thanks!

[@Daily_language]

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

[@Myexpectationsarerealistic]

We usually convert this into a Harmonic series though; as the negative is viewed as being across the Complex Plane, which is generally not useful for probabilities outside of needing statistical inferencing. In many models, they resize to fit between 0-1. Which keeps the size of the integers reasonable.

[@simpleme989]

Extremely well done! thank you for sharing this.

[@avinashanand2163]

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

[@ritvikmath]

Thank you!

[@mrinmoybanik5598]

Thanks for the precise explanation 👍!

[@alisamalakhova]

such a great explanation!

[@nibydlo]

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

[@Mars.2024]

Great as always🌱, would you please mention free datasets or examples of real world projects where we should use the tSNE approach.

[@maitreyimandal8910]

You are a genius!

[@zakariaelkazdam7077]

Amazing explanation , thank you !!!

[@woodworkingaspirations1720]

Beautiful lecture

[@felix1840]

Awesome as usual!

[@diabolo19x]

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

[@ritvikmath]

Great suggestion!

[@FIRETIGER49]

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

[@includestdio.h8492]

Wow... thank u, u really the best

[@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?

[@jaivratsingh9966]

super!

[@张晓峰-m2p]

very good !!!

[@riteshdadlani353]

What a great fucking video

[@mabmab100]

its KŌ-SHE