Dropout Regularization

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Destrezas que aprenderás

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Reseñas

4.9 (61,421 calificaciones)

  • 5 stars
    88,22 %
  • 4 stars
    10,60 %
  • 3 stars
    1 %
  • 2 stars
    0,11 %
  • 1 star
    0,05 %

CV

23 de dic. de 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow

Thanks.

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.

De la lección

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.

Impartido 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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