Dimensionality Reduction : Data Science Concepts

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  • Опубликовано: 21 авг 2024

Комментарии • 16

  • @NarekGevorgyan
    @NarekGevorgyan 2 года назад +3

    man you are a brilliant guy! you explain everything in very understandable language.

  • @michaelprashanth664
    @michaelprashanth664 4 месяца назад

    you are awesome, i am starting to learn scRNAseq and the application of ML in CompBio, this explanation was like a smoothie.

  • @prathmeshlonkar2387
    @prathmeshlonkar2387 5 месяцев назад

    I came, I saw, I understood, I liked, I subscribed!

  • @wvagner284
    @wvagner284 7 месяцев назад

    Thanks a lot for such a great explanation!

  • @trongang8914
    @trongang8914 Год назад

    thanks, your explanation is real help

  • @davidliau2336
    @davidliau2336 3 года назад

    Thanks for the video -- great review material!

  • @JanLaalaa
    @JanLaalaa 11 месяцев назад +1

    wow i got it in the first 30 seconds of the video

  • @jameslucas5590
    @jameslucas5590 3 года назад +3

    I wish you had Method 3, FA. That would be a nice comparison of the three.

    • @BeKindPlox
      @BeKindPlox 3 года назад

      Whats FA?

    • @sunilmathew2914
      @sunilmathew2914 3 года назад +1

      Factor Analysis 🧐

    • @paulseidel5819
      @paulseidel5819 3 года назад +2

      I believe fa is the same as pca except the eigenvectors are rotated.

  • @negusuworku1871
    @negusuworku1871 6 месяцев назад

    very helpful Thank you.

  • @user-or7ji5hv8y
    @user-or7ji5hv8y 3 года назад +1

    Wondering can information theory / entropy be useful in this context as well for dimension reduction.

  • @philtoa334
    @philtoa334 3 года назад +1

    Thx.

  • @sivuyilesifuba
    @sivuyilesifuba Год назад

    Nice👏