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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

57,312 ratings
6,579 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

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

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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5651 - 5675 of 6,506 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Jie Y

Mar 10, 2018

The class should include more introduction on the current ml frameworks such as tensor flow etc. Possibly it should include one more project for the ml framework. Hope to give students more experience on the ml frameworks.

By Deva C R M

Nov 19, 2017

Good and detailed information on how to tune parameters, optimization techniques and regularization. I'm confident that this course learning will help me in training NN to better convergence in a shorter time than earlier.

By Karl S

Jan 2, 2019

I would have liked more details on the math. Furthermore, I think that the discussion of TensorFlow was a bit too short. Although I was able to do the assignment I have not yet developed an understanding of TensorFlow.

By Julien B

Jun 27, 2018

Excellent. Mon regret est que l'exercice final ne mette pas en oeuvre le tuning des hyperparamètres sur un jeu de cross validation. Un exercice supplémentaire avec TensorFlow ou Keras sur cette notion aurait été un plus.

By wilfried l

Apr 11, 2020

Very Interesting

As usual, it is very good from theory point of view. Practical examples are also really interesting.

Do not expect to be autonomous after the course, as you won't be able to use Tensorflow or Keras alone.

By Gil F

Nov 3, 2019

I'd make the tesnsorflow section a separate week with much more elaboration, the first time (in both course 1 and course 2) I felt a subject was lacking information. It's mostly noticeable in the programming assignment.

By Marc D

Sep 14, 2019

The course really takes the student by the hand through the exercises. The disadvantage is that it is not really necessary to understand what you are doing. Just follow the guidance. But on the whole really satisfactory

By Heung K L C

Sep 23, 2018

Very exciting and interesting course overall but the programming assignment with Tensorflow was not practical in my opinion. Instead having practical experience building NN with Keras might have been the better choice.

By Nikolay K

Sep 10, 2017

Generally the course is very good! I liked that I could manually implement the steps of hyperparameters tuning. I wish there was a bit less boilerplate code. Implementing everything from scratch would be more valuable!

By Mark H

Mar 10, 2018

Could be Greatly improved by having us build a NN using previous learning's with the only change being use of SoftMax for Cost. Then have us use TF to do the same and compare the code effort, and the results 1-to-1...

By Gabriel R

Nov 8, 2020

Muy buen curso! Me hubiese gustado que se desarrolle un poco más TensorFlow. No me quedo claro por ejemplo, cuándo hay que inicializar variables, si es realmente necesario definir las constantes con tf.constant, etc.

By Raúl A d Á

May 15, 2020

The explanations are amazing. I do not qualify with 5 stars as I think that practice can be structured in a better way. If the practice is done after each module in each 'week' it would help to retain main concepts.

By Ralf S

Aug 28, 2019

Good course overall. but labs could be expanded. Don't know if the Coursera platform supports it, but labs between lectures about different topics would be nice instead of having all practical exercises at the end.

By Christoph D

Feb 3, 2018

Nice course, as always!

But I think the hyperparameter tuning methods are hopelessly outdated / missing the most promising current developments. A pity since this is such a central part of the actual work with DNNs!

By Yuvini D S

Dec 10, 2019

You can get a better insight as to how to improve neural networks that go beyond the fundamentals. The quizzes and assignments helps you get a hands-on experience of the theoretical material covered in the course.

By Oriel B

Dec 5, 2019


I enjoy the course a lot!

for tensor flow - I am not sure if its me or the course - but I need much more training to start thinking the tensor flow way. maybe i will practice more on real work cases.

thanks !


By Craig M

Oct 20, 2017

You've learned deep neural nets but on the first problem you apply them to they seem to not work or learn to slowly. Don't panic, all you may need is a little fine-tuning, that is what this course will teach you.

By Joakim P H

Sep 4, 2017

After this second course you will be able to start build things using Tensorflow. Really great to see how good this course is structured. Things from course one is comming back making it easy to grasp new content.

By Gemeng Z

Jan 28, 2019

Overall, the course is interesting and introduces systematically technical details. There are still some confusing part in the assignment. For example, the direction in the last assignment is kind of misleading.

By Amir H

Jun 25, 2019

The explanation and examples are very informative throughout the course. The quizzes and the assignments are highly related to the topics covered in the videos which provide a solid understanding of the course.

By Luca V

Jul 25, 2018

Some very interesting consideration, though I would have liked a section about reproducibility and randomisation (including for GPU trainining), though I understand that this is framework and language dependent

By Karl M

Nov 21, 2017

Some of the programming assignments are a bit confusing, and the grader seems to suffer from bugs at the moment. Nevertheless I found especially the part on optimization algorithms very helpful and interesting.

By Barış K

Jan 10, 2021

Maybe TF should be thought a little earlier with small exercises in the weeks 1 & 2. Also the final programming assignment should be improved. The seed initialisation at the Xavier initializer is ambiguous.

By William R

Oct 1, 2017

The insights and intuitions Andrew communicates are good, but as he starts to point out towards the end of this course, in practice one uses a DL Framework and you don't code these things from the ground up.

By Martijn v d G

Jan 5, 2021

The level of detail in this course really leads to a good understanding. A bit more programming exercises with TensorFlow (more than a single model) would be good to understand the intricacies a bit better.