t-SNE Simply Explained

Поделиться
HTML-код
  • Опубликовано: 4 ноя 2024

Комментарии • 32

  • @SasukeBlackHeart94
    @SasukeBlackHeart94 7 месяцев назад +9

    this guy's seriously underrated

  • @SleekGreek
    @SleekGreek Год назад +14

    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

  • @MrErick1160
    @MrErick1160 11 месяцев назад +2

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

  • @jameslucas5590
    @jameslucas5590 Год назад +2

    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

  • @priyaarora7169
    @priyaarora7169 Год назад +1

    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?

  • @hungchen6604
    @hungchen6604 Год назад +1

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

  • @emmanuellawal2694
    @emmanuellawal2694 Год назад +1

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

  • @alisamalakhova
    @alisamalakhova 2 месяца назад

    such a great explanation!

  • @Daily_language
    @Daily_language 7 месяцев назад +1

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

  • @priyaarora7169
    @priyaarora7169 Год назад +1

    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
    @Mars.2024 3 месяца назад

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

  • @avinashanand2163
    @avinashanand2163 Год назад +1

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

  • @mrinmoybanik5598
    @mrinmoybanik5598 5 месяцев назад

    Thanks for the precise explanation 👍!

  • @nibydlo
    @nibydlo 9 месяцев назад

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

  • @diabolo19x
    @diabolo19x Год назад +3

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

  • @simpleme989
    @simpleme989 7 месяцев назад

    Extremely well done! thank you for sharing this.

  • @felix1840
    @felix1840 11 месяцев назад

    Awesome as usual!

  • @woodworkingaspirations1720
    @woodworkingaspirations1720 6 месяцев назад

    Beautiful lecture

  • @shashanksistla5400
    @shashanksistla5400 11 месяцев назад

    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?

  • @zakariaelkazdam7077
    @zakariaelkazdam7077 8 месяцев назад

    Amazing explanation , thank you !!!

  • @FIRETIGER49
    @FIRETIGER49 Год назад +1

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

  • @张晓峰-m2p
    @张晓峰-m2p Год назад

    very good !!!

  • @jaivratsingh9966
    @jaivratsingh9966 6 месяцев назад

    super!

  • @includestdio.h8492
    @includestdio.h8492 Год назад

    Wow... thank u, u really the best

  • @riteshdadlani353
    @riteshdadlani353 11 месяцев назад

    What a great fucking video

  • @mabmab100
    @mabmab100 6 месяцев назад

    its KŌ-SHE