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

4.9
stars
62,857 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

NA

Jan 13, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

AS

Apr 18, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

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5876 - 5900 of 7,218 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By 崔宁

Nov 13, 2017

GREAT

By Jian X

Oct 30, 2017

挺不错的。

By Yates Y

Oct 24, 2017

Nice!

By wangdawei

Oct 15, 2017

good.

By 段立溟

Oct 14, 2017

表白吴伯伯

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

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i

s

a

By Hong W

Sep 24, 2017

Sweet

By Edisson R

Aug 24, 2017

12/10

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 Khôi N N N

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