Gradient Checking Implementation Notes

video-placeholder
Loading...
View Syllabus

Skills You'll Learn

Tensorflow, Deep Learning, Mathematical Optimization, hyperparameter tuning

Reviews

4.9 (59,055 ratings)

  • 5 stars
    88.31%
  • 4 stars
    10.54%
  • 3 stars
    0.97%
  • 2 stars
    0.10%
  • 1 star
    0.05%

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.

CV

Dec 23, 2017

Filled StarFilled StarFilled StarFilled StarFilled Star

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\n\nThanks.

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.