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
63,486 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

NC

Aug 18, 2017

Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

BA

May 31, 2020

Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!I will pass on what I have learned here to undergrads :)

Filter by:

5926 - 5950 of 7,283 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Dave P

•

Oct 10, 2017

Hello

By Shiyuan Z

•

Oct 2, 2017

good!

By 刘其波

•

Oct 2, 2017

good!

By Rafael L

•

Sep 26, 2017

I

t

i

s

a

By Hong W

•

Sep 24, 2017

Sweet

By Edisson R

•

Aug 24, 2017

12/10

By Mohanraj S 2

•

Jun 30, 2024

good

By Omm J

•

Sep 18, 2023

good

By Kiệt H V T

•

Apr 7, 2022

Nice

By 1172陳耑任

•

Mar 10, 2022

good

By Khoi-Nguyen

•

Jan 11, 2022

Good

By 张浩然

•

Sep 5, 2021

good

By Ulan T

•

Aug 14, 2021

good

By 322_PRADIPTA C

•

Aug 7, 2021

good

By 谭博予

•

Jul 7, 2021

good

By 刘铭

•

Jun 28, 2021

good

By Aniruddha B

•

Apr 22, 2021

Nice

By GC L

•

Feb 18, 2021

good

By Ibadurrahman

•

Dec 17, 2020

nice

By Ivri K

•

Dec 11, 2020

nice

By Gulnara A

•

Oct 15, 2020

Best

By Atrayee C

•

Sep 30, 2020

good

By Souvik P

•

Sep 29, 2020

good

By Diwakar S

•

Sep 27, 2020

good

By Pradeep

•

Sep 19, 2020

good