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

4.9
stars
63,221 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

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.

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.

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6801 - 6825 of 7,257 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Misael D C

May 22, 2020

I had some issues regarding coding, but other than that, great!

By Ananthan J

May 11, 2020

Need further explanation on the optimizer with gradient descent

By Miao Z

Apr 6, 2019

Great course, lecture is perfect. assignments could be improved

By wzh

Jan 10, 2019

代码给的答案和自己写代码运行得到的答案不一样,让我想破脑袋想了很久都不知道哪里错了,结果一提交答案评分的时候又说我是对的,头疼

By Rory W

May 28, 2018

Good overview of optimization methods, but moves a little slow.

By Manuel Á S Á

Sep 24, 2021

I felt that the programming assignments were a little too easy

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