i really liked this course,most concepts are nicely explained.

I just think that the part abourt the tensorflow framework should be more developped.

Thank you for this excellent material.

It is a good course. I have learnt a lot from all of the sessions and now I am getting more and more confident in building neural network by myself. BTW, I am ready to take next course.

Dr Ng explained the fundamental things so clearly. In order to be a good developer on machine learning, you need to understand what is going on underneath the framework you are using.

Initially I thought this course to be a simpler course but eventually it turn out be a very conceptual and applied course. So this leads to a lot of learning. In fact Extreme learning!

This course is very useful for the computation of hyperparameters and Neural networks. This is helpful for the intermediate practitioner. I suggest for the go through this course once.

The teacher's explanations are in place and easy to understand. Arranged assignments are also very helpful in mastering the content of the classroom. In short, it's a very good course.

Recommended course for understanding the importance of hyperparameters in Neural Networks and understanding the structure of the optimizers used for training (gradient descent to ADAM)

Wonderfully designed course, I understood RMS Prop and Adam so well that I felt why other articles are so complex. Your explanation on exponentially weighted average is simply awesome.

Great at building foundations in deep learning, I have already worked with Tensorflow some, but now feel I have a better understanding of what the commands are doing behind the scenes.

Great course. I learned a lot, and the exercises although seems a little simple at times, really improves confidence in trying to implement and teaches some good conventions to follow.

For the everyday practitioner of deep learning this course is definitely a must. Professor Ng explains the most important empirical techniques in the day to day use of Neural Networks.

Just like the first course of this series, it is really a very good course. Everything was explained clearly. Not doubt. the highlight of this course is teaching style of Professor Ng.

It's great to know how machine learning algorithms work, but I'm glad I can now also use them with TensorFlow. The practical assignments were very interesting, especially the last one.

Great course that covers the optimization algorithms and advanced hyperparameter tuning concepts in greater depth. The last week also introduces the deep learning frameworks in details

Its a very informative, in-depth course and nicely ties in with the previous course in this specialization. It demystifies the cloud of tuning parameters briefly discussed in Course1.

It was amazing course that showed the importance of optimizing the parameters which improve the performance of the algorithm to a great deal. A great course and an amazing instructor.

Really good teaching and quizzes and programming exercises were of a decent difficulty level. Taught me a lot of things related to deep learning which are applied in various projects.

The insights provided by Prof. Andrew are priceless. I sincerely hope that I get to go in such depths, as taught by him while implementing these algorithms in real-world applications.

The last exercise for TensorFlow was the most fun of all the exercises! Explanations were good, and I still had the opportunity to google documentation to finish some of the functions

I felt like this course gave me a very solid foundation in improving my deep learning models. It tries to touch on the major topics to give you a starting point for your own research.

I love this course. It has many in-depth tips and advices based on many real life experiences. Many suggestions can be applied directly into solving difficult Deep Learning practices.

This is much needed course with great content on improving neural networks which is rarely available in any other online course. Suggestions from andrew Ng's experience is invaluable.

the course is great, truly continuation of the first course. starting with hyperparameters tuning then how to speed up the process, finally use DL framework to efficiently build model

Absolutely necessary! Although they were stronger on Regularization and Optimization than on Hyperparameter tuning, one cannot go about using ML/DL without those concepts amalgamated.

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to make them work efficiently.