Chevron Left
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

4.9
stars
63,175 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

AM

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

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.

Filter by:

6526 - 6550 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Arkosnato N

•

Nov 28, 2017

course content was very good, but this course should be longer. there was a lot of material covered in a very short time.

By Michael B

•

Mar 17, 2018

More pragmatic approach with theorems would be more appealing....or maybe it is me as i'd prefer Java (DL4J)...not sure

By Santiago F V

•

Jul 2, 2020

The theorical part is perfectly explained. However, the program assingment of the las week is not as good as expected.

By Nguyá»…n Q T

•

Jun 21, 2020

Thanks a lot for clearly explaining of intuition about algorithms and optimizer. More ever, great design of assignment

By Avinash V

•

May 4, 2020

Outstanding material. Would like to thanks Mr. Andrew Ng Sir for providing such a nice and detailed description.

THANKS

By Vivi M

•

Oct 29, 2017

I really enjoyed the classes, in the training I would've liked to try and improve the model with all the tools learned

By Amit J

•

Nov 22, 2019

Great practical insights.

I wish there were programming assignments on "Hyperparameter tuning" and "Batch norm" too.

By Christopher S

•

Oct 25, 2019

Good intro to the available tools. Very guided course. For concepts to really stick, own projects or courses needed.

By George L

•

Oct 24, 2018

it's good, but definitely not as good as the first course since Prof. Ng was not very clear on some of the concepts.

By Ruixin Y

•

Apr 30, 2018

The course itself is great, but the notebook (programming assignment system) is not stable, it's annoying sometimes.

By Péter T

•

Apr 17, 2018

Useful information, good intuition, but lack of formal results. More homework would improve the learning experience.

By Ashutosh P

•

Apr 4, 2018

It was a great course. Really well taught by Professor Andrew Ng. Some "from the scratch" coding assignments needed.

By Suresh D

•

Feb 28, 2018

I hated the tensorflow part though. Would have much preferred it if we could have moved away from jupyter notebooks.

By Francisco C

•

Jul 24, 2018

Very good content overall. Very well explained and good examples. Many mistakes in the comments in the assignments.

By Abhinava K

•

Dec 8, 2017

Content is good, but assignments are not interesting. Some application oriented assignments will be be encouraging.

By Julio T

•

Sep 7, 2020

Very good course, all relevant and well explained. I think it just needs and update for working with TensorFlow 2.

By manish c

•

Jan 23, 2020

Like all other andrew ng courses this course is also the best course to deep dive into neural network algorithms .

By Francesco P

•

Feb 26, 2019

I would like to see more programming assignments. They are very well done and it'd be great to have more of those.

By Angad S

•

Dec 13, 2017

I would really benefit from this course if more assignments are provided to try different data sets and scenarios.

By Christopher G

•

Feb 23, 2024

Great course. Some guidance on implementing backpropagation with batch normalization would have been appreciated.

By Rahad A N

•

May 13, 2020

Absolutely love the course and the way Andrew teaches us, though I have a little bit discomfort in writing codes.

By Emmanuel

•

Mar 5, 2020

A little bit to theorical and with too many guidance at some points and not much at some other (for TF functions)

By Giovanni C

•

Feb 11, 2019

I liked the course, but the explanation of tensorflow needs more propaedeutic introduction for a learner like me.

By Charbel J E K

•

Jan 17, 2018

Really helpful ! Too much concepts to understand but only applying few in the course. I really liked this course.

By Jay R

•

Dec 24, 2017

Good course to get familiar with hyperparameters and improving the neural networks. And cliff hanger was amazing!