Why Regularization Reduces Overfitting?

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Habilidades que você aprenderá

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Avaliações

4.9 (61,462 classificações)

  • 5 stars
    88,21%
  • 4 stars
    10,60%
  • 3 stars
    1%
  • 2 stars
    0,11%
  • 1 star
    0,05%

JS

4 de abr de 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

AB

26 de ago de 2021

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

Na lição

Practical Aspects of Deep Learning

Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.

Ministrado por

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    Andrew Ng

    Instructor

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    Kian Katanforoosh

    Senior Curriculum Developer

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    Younes Bensouda Mourri

    Curriculum developer

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