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
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?
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
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?
this guy's seriously underrated
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
Thanks! 😃
I don't think I've ever been explained something so well. I feel like a genius right now! thank you haha
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
thanks!
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?
Thanks for the great explanation, especially on why using Cauchy in the low-dim space!
Glad it was helpful!
I'm just flooding my brain with new information, I believe I'll understand it indue time. Nice video as always
thanks!
such a great explanation!
very clear explanation and help me understand t-sne. Great job! Subscribed your channel
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
Great as always🌱, would you please mention free datasets or examples of real world projects where we should use the tSNE approach.
Amazing Content. Please make more videos, your videos are so easy to understand. Thanks a lot
Thank you!
Thanks for the precise explanation 👍!
This wasn't easy, but finally I got the idea. Thank you!
Super video! What about a video on the maths and intuition behind variational auto encoders ?❤
Great suggestion!
Extremely well done! thank you for sharing this.
Awesome as usual!
Beautiful lecture
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?
Amazing explanation , thank you !!!
you dint over-explain believe me! XD. Overall superbly explained, thank you very much!
very good !!!
super!
Wow... thank u, u really the best
What a great fucking video
its KŌ-SHE