Numerical Approximation of Gradients

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Получаемые навыки

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Рецензии

4.9 (оценок: 61,607)

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

NA

13 янв. 2020 г.

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.

JS

4 апр. 2021 г.

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.

Из урока

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.

Преподаватели

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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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