Still some errors in the assignments, particularly in Week 3. Otherwise, a good course. A lot of good topics for practical use when building real-life neural networks. Some seem fairly cutting-edge. Good, brief, introduction to TensorFlow at the tail-end of Week 3 and in the programming assignment for that week.

The course content is really great and the theoretical concepts (or their intuition to a larger extent) have been explained pretty well. But there are some errors in the programming exercise on Tensor Flow which makes it confusing since the people who take this course are new to both deep learning and tensor flow.

This is a great course. The only reason for not giving it 5 stars is the notebook platform for grading coding assignments. It is flawed in that attempts to save often error out so a submission often doesn't contain the latest state of the code. This causes sections to be graded incorrectly - very frustrating.

Course material and Lectures is perfect!

May be would like to have less supervised programming assignments with less hints of how to implement code as it was too easy! May be it would be good to have pretty guided assignments during main part of the course, but something more individual and demanding at the end.

I think it is important to understand the basics and that is why it is really cool that they show you how to implement a neural net from sratch. Moreover, the last part on tensorflow shows you how to do it in real life. Nevetheless, a lot of work remains to be done to learn properly how to use tensorflow and NN

Andrew Ng is great at explaining the theory and practical aspects of DL concepts. I applaud him for making the lectures so accessible. I also really liked that he provided what is typically done by practitioners. My only feedback I have is that the quiz and programming assignment could have been more rigorous.

the programming assignments are too easy. Though I understand that they're supposed to guide someone who may be a total beginning in ML and DL, I feel there is still too much hand-holding by marking exactly the changes need to be done and the formula either embedded in the comments or in the description above

Brilliant. The way this whole course was structured, the correctness of everything, and the amount of thought and preparation that has gone into this is amazing. Thank you.

One suggestion : for those who would want to fiddle with math, there could be optional links to read / understand derivations and stuff.

I understood in detail about the various hyper parameters and how to tune them in order to obtain the best results. I learnt about the problem of over fitting and how to solve it. I also got a glimpse of the tensor flow framework and understood how it makes the process of creating neural networks easier.

Great until the last weeks exercise. It definitely needs to be improved. Some of the instructions do not make sense and there are errors in the outputs that make it confusing to follow. However, I did complete it in 1 hour where it said it would take 3. That being said, all videos were amazing as always.

The content for the first two weeks can be thoroughly practised using the programming assignments but for the 3rd week there is a huge gap in the practise material provided.The majority of the content taught during this week is not covered in the programming assignment.Hope this gets seen and rectified.

While the first course in Deep Learning Specialization gives us the fundamental insight, this course shows us the practical aspects such as hyper-parameters tuning and mini-batching . I think it is essential for beginners to take this course if they actually want to apply Deep Learning to their domains.

Generally great course! But I think more tutorials about Tensorflow are needed. I suppose by using the current tutorial in this course, no one except those who have used Tensorflow before, is able to apply Tensorflow to their own work. The programming exercise is more like a

shallow experience so far.

The course was useful for me as a beginner in the deep-learning domain. The instructor was very clear and easy to understand. In order for the student to easily grasp some concepts, useful intuitions were provided. However, being a mathematics student I would have appreciated a more thorough analysis.

Very good overview of optimizer functions and hyperparameter search. I felt the Tensorflow portion could be better with more emphasis put on what the TensorFlow programming construct is as thinking in terms of computational graph and functional programming aspect could be covered as a general overview

The courses are good and impart knowledge from the experience of Dr. Andrew Ng. However, I wish they had more of "After the lecture" 1-2 question quizzes [they have such quizzes designed in course 1 of deep learning specialization].

These quizzes bring up the level of course (and learning) infinitely.

Again, A very good course , it focuses on building the concepts from fundamentals. I got very intuition of the way neural nets work.

The Jupyter notebooks are self explanatory and comprehensive. Though I think programming could have been a little tougher, could have used a fully hands on assignment.

Starting to get somewhere.. This course contains a bit more interesting topics and I did not feel it was as overlapping to the Ng Machine Lerning course as the first course in the Deep Learning specialization was. Also, finally, a programming task is done using a deep learning framework (TensorFlow)

Good material as always from Andrew Ng. There is a lot more "eyes-forward lectures" before the coursework on this one, which didn't suit my my personal learning preference as well (I prefer to get a lot of practise in when getting to grips with new concepts). Good course though, worth the effort.

I feel like the assignments involve a lot of cut and pasting of functions verbatim. It's a good start, but I'd like to go further and be challenged to solve a problem, rather than fill in the blanks. For example, try different optimizers to reach a particular learning speed on a given data set.

Hands-on projects are very easy. The only ask for copying and pasting some lines of code. In contrast, questionnaires are well designed and reprepresent the knowledge acquired. Another course drawback is that the last programming practice employs TensorFlow 1, the version 2 was released in 2019.

Everything was fine but there were some issues that was not pleasant to me. like seeing that there's problemmes with the videos and they're not corrected and instead there's an extra reading about that. Or when Tensorflow 2 has come, I was expecting that the course also introduces newer version.

The course and the instructor are amazing, but I fell behind schedule by one week and the last assignment was locked, and when attempting to reset the deadline, a message saying "something went wrong" would pop up. The help center responded very quickly and professionally, and solved the issue.

This course I believe is one of the most vital one after the first course in the specialization! Professor Ng covers all the concepts required for you to understand and master this course.

You might have to refer to additional resources to get a complete grasp of the concept post each video.

Great and easy-to-follow introduction to improving deep neural networks. If you are already familiar with vector algebra, many things are explained quite slowly. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.