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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.

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5851 - 5875 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By hexinlin

Nov 29, 2018

great

By 钟胜杰

Nov 18, 2018

good!

By beihai Q

Nov 12, 2018

Good!

By Vinita M

Oct 26, 2018

Great

By Simon H

Oct 7, 2018

Great

By shenzhonghua

Sep 3, 2018

Great

By 노재화

Sep 2, 2018

Good!

By He L

Jul 7, 2018

cool!

By Ashish B

Jun 24, 2018

great

By jiahaozhou

Jun 11, 2018

grate

By sachin

Jun 9, 2018

super

By blaster_fire

May 24, 2018

Good!

By Du L

Feb 24, 2018

Best!

By Won J

Feb 19, 2018

Great

By benedikt h

Feb 18, 2018

Great

By lalaqingla

Feb 8, 2018

good!

By 冉祥映

Feb 5, 2018

nice.

By Minsub W

Feb 1, 2018

good!

By qingjunwu

Dec 20, 2017

Good!

By Siddarth V

Dec 11, 2017

Great

By iron

Dec 7, 2017

great

By 董雪振

Dec 3, 2017

Good!

By Hiep N V

Nov 18, 2017

G

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e

a

t

By 崔宁

Nov 13, 2017

GREAT

By Jian X

Oct 30, 2017

挺不错的。