Kernel Function in Support Vector Machine SVM || Lesson 83 || Machine Learning || Learning Monkey ||

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  • Опубликовано: 1 фев 2025

Комментарии • 30

  • @deveshnandan323
    @deveshnandan323 Год назад

    Awesome Explanation Sir , the way you dive deeper in the crux of concepts is Amazing :)

  • @JaySharma-se7qv
    @JaySharma-se7qv Месяц назад

    Best ML playlist

  • @sivakumar-rz9nc
    @sivakumar-rz9nc 2 года назад +1

    system approach to concept, that makes you unique, ji

  • @aniketgokarn3941
    @aniketgokarn3941 4 года назад

    AMAZINGGGGGG Explaination :D Thank You :D

  • @33333ashish
    @33333ashish 3 года назад

    great work...

  • @mouleshm210
    @mouleshm210 4 года назад

    I have a small question,
    for both linearly separable data and non linearly separable data,
    1) --> Does sklearn's SVC() internally solves only dual problem or the primal problem(incase of linearly separable data) ?

    • @LearningMonkey
      @LearningMonkey  4 года назад

      Here it is solving using gradient descent.
      Dual form equation Is used

    • @mouleshm210
      @mouleshm210 4 года назад

      @@LearningMonkey thankyou sir..

  • @shantanuekhande4788
    @shantanuekhande4788 4 года назад

    hi sir thanks for this video

  • @shobanados7693
    @shobanados7693 3 года назад

    ❤️❤️

  • @TheS4akella
    @TheS4akella 4 года назад +1

    Nice

  • @ahmedalaa4699
    @ahmedalaa4699 3 года назад

    thank you very much your explanation is amazing
    can you give me the resource if you have up till now

  • @KapilKumar-tw6xn
    @KapilKumar-tw6xn 4 года назад

    Sir, please make the videos on polynomial kernel and RBF kernel.

    • @LearningMonkey
      @LearningMonkey  4 года назад

      Hi kapil
      Present we are doing deep learning.
      After completion of deep learning we do that.
      Have a great learning

  • @milandudhat2051
    @milandudhat2051 3 года назад

    i understand your concept but still i didn,t get completely. so i have one example that how to solve.
    Ex:ample: Let x and y be two vectors. The two vectors are transformed into a high-dimensional space using a kernel function defined below. Compute K(x,y) in order to fill the values for a and b.
    ϕ(z)=(1,√2z1, √2z2, z1^2, z2^2, √2z1z2)
    K(x,y)=

    K(x,y)=(a+(x.y))^b
    then what is the value of a=?, b=?

    • @LearningMonkey
      @LearningMonkey  3 года назад

      Hi milan
      You can take a, b different values and try it as hyperparameter

    • @milandudhat2051
      @milandudhat2051 3 года назад

      @@LearningMonkey ohk Sir Thank you very much.

  • @hemangdhanani9434
    @hemangdhanani9434 3 года назад

    sir, waiting for your polynomial kernel and RBF kernel. can you please upload it...thanks

    • @LearningMonkey
      @LearningMonkey  3 года назад

      We are dng our best. It takes time to complete remaining

    • @hemangdhanani9434
      @hemangdhanani9434 3 года назад

      @@LearningMonkey, thanks for the confirmation, really appreciate the way you are teaching...hats off to you

    • @LearningMonkey
      @LearningMonkey  3 года назад

      Have a great learning

  • @adityarajora7219
    @adityarajora7219 2 года назад

    how to get alphaz????? I'm not getting one thing How kernel helping in getting alphas??????

    • @LearningMonkey
      @LearningMonkey  2 года назад

      Alphas are identified when we solve the optimization problem.
      Check karush khuntuker example video.
      U will get idea

    • @adityarajora7219
      @adityarajora7219 2 года назад

      @@LearningMonkey ok, thanks for your hard work : )

  • @sankarancs3849
    @sankarancs3849 3 года назад

    What is xsubj in optimisation problem sir

    • @LearningMonkey
      @LearningMonkey  3 года назад

      Input data row.
      It's explained in previous videos.

    • @archanamaurya89
      @archanamaurya89 2 года назад

      @@LearningMonkey Actually the notation is a bit confusing. As per the table in this video, x_i, x_j would be cross-product of columns but a and b are cross-product of rows..how is that possible?

  • @kewtomrao
    @kewtomrao 3 года назад

    Transformed data space belongs to R^3 here but ends up in R^2 space.Hence computationally less expensive!