Gradient Checking

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Compétences que vous apprendrez

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Avis

4.9 (61,618 évaluations)

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

AM

8 oct. 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

HD

5 déc. 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.

the only thing i didn't have completely clear is the barch norm, it is so confuse

À partir de la leçon

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.

Enseigné par

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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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