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

4.9
stars
63,156 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

HD

Dec 5, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.

the only thing i didn't have completely clear is the barch norm, it is so confuse

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6801 - 6825 of 7,249 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Shiv V P

Jul 4, 2020

4 stars due to tensorflow 1 assignment instead of tensorflow 2

By Ryan C

May 8, 2020

A little too much hand holding in the programming assignments.

By vaibhav g

May 5, 2020

Tenserflow section could have been a little bit more elaborate

By Nicolas L

Jan 25, 2020

programming assignment should be more open, with less guidance

By Anway A

Aug 3, 2022

Brilliantly laid out. Simple and challenging at the same time

By Mikhail G

Apr 15, 2020

Very quickly jump to almost profffi TF. It's little suddenly.

By Thomas J D

Nov 8, 2018

Little less well structured/organized than the first course..

By Qu S

Oct 27, 2018

感觉讲到tensorflow框架这块儿的时候跳跃有一点点大,如果tensorflow相关的联系更丰富一些,说明更多一些就了

By Anirudh L

Jun 28, 2018

not very happy about tensor flow introduction. rest was great

By Serdar K

Jan 31, 2018

This was helpful. I advise spending more time on tensorflow.

By Filippo M

Jan 16, 2021

Small and fast course, but a good introduction to Tensorflow

By Dinesh m

Sep 26, 2020

More assignments would make this course even more productive

By abhishek s

Jun 22, 2020

not exactly a basic level course, its an intermediate course

By VIGNESHKUMAR R

Oct 23, 2019

Good but need to improve number of examples about tensorflow

By Mark

Oct 10, 2018

Good course but a bit more detailed explanations were needed

By SANAPALA S

Sep 28, 2017

good but would have been great if tensorflow is covered more

By Henry V

Sep 24, 2017

A very good introduction, but a bit basic for professionals.

By Duberney L R

Jun 13, 2022

Deben mejorar las presentaciones y la traducción al español

By Joao V

Nov 17, 2020

I hope I get to learn more about TF in the upcoming courses

By Suyash J

Jun 11, 2020

Very Good Course, best if include notes for quick revision.

By Vishakha S

Feb 5, 2020

I think a short video on tensorflow might help the learners

By Ernst H

Jul 7, 2019

Obvious problems. Lessons and quizzes need to be polished.

By Nick R

Jan 7, 2018

Necessary background information and how-to for algorithms.

By Nimish P

Dec 9, 2021

Great explanations. A few more assignments could be given.

By Deleted A

Jun 16, 2020

Loved the course, content and assignments are really good.