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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

58,194 ratings
6,702 reviews

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

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.

Oct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

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6576 - 6600 of 6,627 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Aashita G

Jun 1, 2020

fast paced not enough emphasis on topics

By Amod J

Mar 18, 2018

Want to download my own work but cannot.

By Rachana O

Aug 17, 2020

Can be done in more interesting manner.

By Mark L

Jul 16, 2020

great superficial intro to the content

By Jérôme C

Oct 14, 2018

Need more training on Tensorflow, imho

By Juan J D

Sep 11, 2017

tensorflow subject was to superficial


Jul 26, 2020

Can dive deeper into the mathematics

By Trevor M

Nov 23, 2020

good lectures terrible exercises

By Maisam S W

Oct 4, 2017

I still find tensorflow hard.

By Andrey L

Oct 1, 2017

week 2 was extremely boring

By Cheran V

May 9, 2020

Outdated with Tensorflow 1


Mar 11, 2018

Again, nice videos but not

By Adam G

Jul 11, 2020

Multiple grading issues.

By b19077 b

Jul 1, 2020

could be more engaging

By Patrick C N

Jan 8, 2020

Update for TF2.0 :)

By Алексей А

Sep 7, 2017

Looks raw yet.

By Ilkhom

Mar 21, 2019

awful sound

By Akhilesh

Mar 14, 2018

enjoyed :)

By zhesihuang

Mar 3, 2019



Jul 14, 2018


By Long H N

Feb 12, 2019


By KimSangsoo

Sep 17, 2018


By Sébastien C

Aug 4, 2020

Course covers the most important parts of hyperparameter tuning, regularization and optimization.

As a general remark for this specialization, the exercices do not provide any value. We just have to fill in some lines and submit our work.

As I tend to "learn by doing" I had to look for other tutorials and projects on other platforms (Kaggle, MachineLearningMastery's website) in order to complete my learning.

By Fabrizio N

Dec 7, 2018

Good course content and clear exposition by Andrew. The course material however is not of a good standard. The slides can be downloaded but after all the hand scribbles by the tutor, they are barely decifrable. Some are just blank pages that need to be filled in with screenshots from of the videos. The assignements are often just a copy and paste exercise, and Jupyter crashes cause frequent loss of work.

By Goda D R

Feb 14, 2020

The video content is very good to get a good hang of theoretical aspects but the programming assignments are too spoon-fed because of which after doing filling the blanks, you don't feel confident enough to implement the same on your own. Instead the assignments should be changed to cases where instructions are given in words and entire function should be implemented by students.