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

4.9
stars
63,157 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

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

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.

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5876 - 5900 of 7,249 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Gzq

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Mar 10, 2019

great

By Hernan R

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Mar 6, 2019

Good!

By Yuhao L

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Jan 12, 2019

good!

By hexinlin

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Nov 29, 2018

great

By 钟胜杰

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Nov 18, 2018

good!

By beihai Q

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Nov 12, 2018

Good!

By Vinita M

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Oct 26, 2018

Great

By Simon H

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Oct 7, 2018

Great

By shenzhonghua

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Sep 3, 2018

Great

By 노재화

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Sep 2, 2018

Good!

By He L

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Jul 7, 2018

cool!

By Ashish B

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Jun 24, 2018

great

By jiahaozhou

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Jun 11, 2018

grate

By sachin

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Jun 9, 2018

super

By blaster_fire

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May 24, 2018

Good！

By Du L

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Feb 24, 2018

Best!

By Won J

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Feb 19, 2018

Great

By benedikt h

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Feb 18, 2018

Great

By lalaqingla

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Feb 8, 2018

good!

By 冉祥映

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Feb 5, 2018

nice.

By Minsub W

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Feb 1, 2018

good!

By qingjunwu

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Dec 20, 2017

Good!

By Siddarth V

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Dec 11, 2017

Great

By iron

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Dec 7, 2017

great

By 董雪振

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Dec 3, 2017

Good!