Dropout Regularization

video-placeholder
Loading...
Просмотреть программу курса

Получаемые навыки

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Рецензии

4.9 (оценок: 61,445)

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

AM

8 окт. 2019 г.

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

AS

18 апр. 2020 г.

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

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

  • Placeholder

    Andrew Ng

    Instructor

  • Placeholder

    Kian Katanforoosh

    Senior Curriculum Developer

  • Placeholder

    Younes Bensouda Mourri

    Curriculum developer

Ознакомьтесь с нашим каталогом

Присоединяйтесь бесплатно и получайте персонализированные рекомендации, обновления и предложения.