Dropout Regularization

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Kompetenzen, die Sie erwerben

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Bewertungen

4.9 (61,421 Bewertungen)

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

NA

13. Jan. 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

CV

23. Dez. 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.

Aus der Unterrichtseinheit

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.

Unterrichtet von

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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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