Chevron Left
Back to Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
62,825 ratings

About the Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

XG

Oct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

JS

Apr 4, 2021

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.

Filter by:

5576 - 5600 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Jagannadha P N

May 30, 2019

Excellent

By 刘盾盾

May 28, 2019

深入浅出,十分受益

By Aru

May 22, 2019

good good

By Nazarii N

May 19, 2019

thank you

By Payne Y

May 12, 2019

very good

By aleks j

Apr 22, 2019

Brilliant

By Aman W

Apr 3, 2019

Excellent

By Deleted A

Mar 31, 2019

Excellent

By Matthew J B

Mar 29, 2019

Fantastic

By jinpengcheng

Feb 18, 2019

excellent

By Yannik L

Jan 9, 2019

Excellent

By 黄怡欣

Dec 11, 2018

very good

By 韩颖君

Nov 1, 2018

VERY GOOD

By 郑睿

Oct 26, 2018

very well

By Pankaj P S

Oct 6, 2018

Loved it!

By Jian H

Oct 2, 2018

fantastic

By SHASHANK P

Sep 23, 2018

EXCELLENT

By ignacio v

Sep 14, 2018

Ng rocks!

By Ethan

Sep 1, 2018

Great sir

By Al T

Aug 30, 2018

Excellent

By Zheng Z

Aug 25, 2018

Very good

By Thomas A

Aug 17, 2018

excellent

By nish2288

Jul 20, 2018

Awesome !

By Fran T

Jul 9, 2018

Loved it!

By Rajdeep

Jun 17, 2018

Thank you