Neural Network learns sine function in NumPy/Python with backprop from scratch

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  • Опубликовано: 5 окт 2024

Комментарии • 10

  • @m3DsStudio
    @m3DsStudio Год назад +3

    Du warst vor 5 Jahren in Braunschweig mein Thermo-Tutor und hast damals schon durch deine Gabe geglänzt, komplexe Sachverhalte verständlich und vor allem mit einer ansteckenden Begeisterung rüberzubringen. Freut mich sehr zu sehen, dass du weiterhin Spaß daran hast!

    • @MachineLearningSimulation
      @MachineLearningSimulation  Год назад +1

      Vielen Dank 😊
      Ist echt schon lange her... Das waren noch Zeiten im Grotrian.
      Habe schon immer gerne unterrichtet, jetzt halt mit etwas fortgeschrittenen Themen, freut mich sehr, dass es dir weiterhin gefällt :).

  • @JudeLight-h7i
    @JudeLight-h7i Год назад +2

    Awesome video as usual!! I been following your channel for a while now and all I can say is Amazing Content. I was sending if you could make a video on Fluid-Structure Interaction Simulation such as deformable beam that would be great.

    • @MachineLearningSimulation
      @MachineLearningSimulation  Год назад

      Thanks a lot 😊
      Great idea, FSI is an interesting topic. Unfortunately, I don't have any experience with it; could be a topic for the far future of the channel since I'm curious in general to learn something new.

  • @corneliusotchere719
    @corneliusotchere719 Год назад +2

    great tutorial. Should the last line in the last layer not be y4hat =I(y4tilde)?

    • @MachineLearningSimulation
      @MachineLearningSimulation  Год назад

      You're right 👍
      Could you open a pull request on the file in GitHub, then I would merge it.

  •  Год назад +1

    Amazing explanation. Thank you!

  • @phurisottatipreedawong1618
    @phurisottatipreedawong1618 Год назад +1

    Thank you very much.
    I have also tried to implement various type of ML algorithms and write the document about it in Google Colab too.

    • @MachineLearningSimulation
      @MachineLearningSimulation  Год назад +1

      You're very welcome 😊
      That's the best way to learn: to implement these algorithms for simple problems yourself. 👍