Great course giving insight on how to fine tune deep neural networks. I believe the contents need to be a bit polished but that's totally understandable given its early stage. The comments in the discussion group will for sure help to fix some typos and make this course even better.

This course helps me a lot in tuning hyperparameters in training machine learning model, just one issue is the last programming exercise when using framework the guide is missing something, which is hard for some to complete, for me, I have to use Google Search to find the solution

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.

Thanks

A useful class delving into the nuts and bolts of building a reliable nn. Well structured and explained. I feel like the use of Jupyter in the homework makes it simpler than it should be. A large portion of the code is already written and the instructions often give up the answer.

For a person, who know a little on deep learning, I learned lots of things or, at least, got a clearer view on many concepts. A little reproach on the notation system : question on quizz sometimes might not be very clear for me - and the flaws of the grader on the assignments.

A very good course for taking understanding of Deep learning one level above the basics. The course is theoretical, but the team has done their best to make it as much hands-on as possible.

I did face some intermittent platform issues with saving and submitting my assignments.

Helps to have a structured approach towards tuning the hyperparameters rather than randomly doing. Also the course also helps understanding why such tuning is necessary and what improvements are being made in the model. Useful course but not suitable for beginners in ML/DL.

Material is awesome like all courses professor Andrew does. But (a) programming assignments are in style 'fill a couple of lines in 90% ready code' instead of end-to-end developing with guidelines and (b) there are a lot of mistakes in subtitles, assignments and even videos

The theory was good, but I think jumping to tensorflow at the end wasn't a logical, progressive step forward. They should have solidified the concepts more thoroughly, rather than jumping to the Tensorflow API. One would really want to have Tensor flow in a separate MOOC.

A lot of content was repeated from the Machine Learning course by Andrew Ng on Coursera. Also, more on TensorFlow and other frameworks implementation would be interesting to learn. But at the end of the day, I did learn a lot of interesting aspects of deep neural networks.

Course is very good especially when revealing "secrets" of various optimization techniques. Once again programming excercise is rather easy to pass as you are guided step by step so there is no space for serious mistakes. More "open" excercises/chalenges would be desirable

This one took a little longer than usual to complete, It took more willpower to come back to it and continue in the course. It seemed harder, or explained lesser than the first course. Nevertheless, after spending extra time on it, Ive finally completed it. Thanks Andrew!

Parameters tuning is ok to follow, it would be easier if you have numerical methods basics. But Tensorflow is not easy to deal with. Maybe it need a separated course. I will get through to programming assignment again to understand it clearly with tensorflow manual pages.

The second class on machine learning is still very informative. However, it's very hands-on and teaches me mainly how to tune learning algorithms to run faster. Hence, it's not very intellectually stimulating. Nonetheless, this is still a very educational course overall!.

The course is great. Andrew is one of the best tutors one could get.

The only reason I rate it with 4 stars is that the TF assignment is not updated to TF2. TF 2.0 with Keras really is a state-of-the-art framework and imho there is not much value in learning TF1 anymore.

Great sequel to Neural Networks and Deep Learning. Relatively short course and the most relevant topics in Deep Learning are reviewed. You also practice with TensorFlow, a well-establish Neural Network programming framework that is widely used in academia and industry.

This course had more of a getting-into-the-weeds feel to it, without as much of the broader conceptualizations that the first course had. I also submitted several queries in the forums without getting feedback. Still good, but I hope the following courses are better.

Overall, this course includes many useful techniques of how to further improve the basic DNN. Just one minor point imo that the tutorial on TensorFlow may need to go deeper for those techniques mentioned previously in this course, for instance implementing batch norm.

Great videos but wish there were more per-lesson exercises that were there in Course#1 for this track. Also, the transition to TensorFlow was quite abrupt as the key concepts that TF uses are completely new and don't easily borrow from the much cleaner Numpy concepts

Decent intro to tuning neural networks. I felt the parts on normalization and regularization could have gone into more detail, but perhaps the math was deemed too complicated. Labs are ok, but still a bit buggy despite errors being reported in the forums a while ago.

This is another great introductory level course. Andrew covers a lot of very practical concepts. This course also builds well on the previous course in the specialization. The only reason I gave 4 stars rather than 5 was that the programing exercise still uses TF 1.

A good theoretical explanation and good working assignments that impart basic understanding of different optimization methods, hypertuning methods and tensorflow framework. But, some mistakes in the tensorflow assignment in the script itself, needs to be rectified.

Andrew Ng presents a very organized course; I would have enjoyed actually iterating on hyper parameters to find the optimal set. Also, there are probably other optimization approaches that would enable simultaneous searching for an optimal collection of parameters.

Overall, very clear teaching from the Great Mr NG, only concern is that the markdowns in the assignment three coding assignments have some mis-aligned expected output from what is actually expected, so there is some confusion. Except that, all is great. Thank you.

It was very helpful to learnt batch normalization, regularization and tensorflow. It definitely needs a lot of self study to learn about these topics for people who are not familiar. Some mathematical resources like links to pdfs and videos would be extra helpful.