Numerical Approximation of Gradients

video-placeholder
Loading...
View Syllabus

Skills You'll Learn

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Reviews

4.9 (59,911 ratings)

  • 5 stars
    88.30%
  • 4 stars
    10.55%
  • 3 stars
    0.97%
  • 2 stars
    0.10%
  • 1 star
    0.05%

HD

Dec 5, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

JS

Apr 4, 2021

Filled StarFilled StarFilled StarFilled StarFilled Star

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.

From the lesson

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.

Taught By

  • Placeholder

    Andrew Ng

    Instructor

  • Placeholder

    Kian Katanforoosh

    Senior Curriculum Developer

  • Placeholder

    Younes Bensouda Mourri

    Curriculum developer

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.