Some lectures seem to have inconsistent/unexplained differences in the math written. For example, I am a bit confused as to whether normalization is done as (x - mean)/variance or (x - mean)/std.dev. Otherwise, excellent content as always!

In some other courses there was a pdf document at the end of the courses which very good if you want revisit them but in these courses its not available. Please make them available here which will be a very time saving for quick revisions

In overall, the course content is helpful and inspiring as normal, and can be used to real life straight away. However there are several typos/mistakes in the assignment, especially in assignment 3 which I had bad time to experience with.

The best course in deep learning: Hyperparameter tuning, regularization and Optimization. The course is best among all the available courses over internet but it lacks availability of study materials (or reference to reading materials).

Very good content. Professor Ng covers a lot of material in a gentle and steady way. A few errors in the assignment and less clarity on some texts and quiz make me give 4 stars, but overall it's a very useful, important course, I think.

Great course for in-dept understanding of parameter tuning and optimization, +tensorflow. I would recommend increasing the complexity of the programming assignments. At this point we should be controlling more of the basic python setup.

Maybe not as exciting as the first course of this series (Neural Networks and Deep Learning) as this one delves more in the "technicalities" of NN. The presentation of the topics, however, is always very clear and easily understandable.

Struggled a bit to grasp the batch nomalization, Initially Regularization was also hard to grasp the first time, subsequent viewing made it clear though but batch norm still is a bit hazy. I am happy though we are in to Tensorflow now.

The material was excellent for this class and so were the lectures. I think more programming assignments could have been optimal though. There are so many concepts, and I think there are several pieces we didn't implement in practice.

There were still a few times where I felt some clever editing could have hidden math errors but I felt the second part was already more polished and accessible than the first. I hope the rest of the series continues in this direction.

One of the best and most technical course in this Specialization: I enjoyed learning a lot on optimization algorithms. Really good practical hints on tuning and on bias variance analysis, that are very difficult to find in textbooks

Indeed this is an awesome course for any beginners in deep learning.One suggestion could be is why you have selected Tensorflow framework.Will it be possible to get same assignment in Pytorch framework which out institiute follows.

This course was excellent, however the Tensor flow at the end feels a little bit like the ML field is quickly being overtaken by the frameworks, and the Tensor flow section is a little bit tacked onto this course, maybe in a hurry.

Interesting practical suggestions regarding hyperparameter tuning and batch normalization, it could be more mathematical with more programming assignments with the effects of tuning. The TF framework was kind of surprise in Week 3

As always, great course from Andrew: easy to be understood, useful trainings and exercices. The lecture are explained slowly and repeating the important concept, always a good think.

Thanks! I will proceed with my Specialization :)

The content of the course is excelent and the practice exercises very interesting and helpful. I feel there is missing a written resume of the concepts after each video, or a hand-in of the powerpoint presentations would be nice.

Pros: The course covers the basics for hyperparameters, tunning, regularization and optimization. The basics that it covers are well presented and explained.

Cons: A much more detailed and intense work with TensorFlow is needed.

The course is really good and the explanation by Dr Ng is exhaustive. But I think the assignments were meant for beginner level. You will not be implementing any function instead you be writing the underlying formulas. That' it.

This is another great introduction to Depp learning frameworks apart from all the neural network performance upgrading techniques taught. This is an excellent course for building a strong foundation of deep learning fundamentals

This course was mostly well-designed especially for the first topics, but in my view, the Tensorflow part needs to be extended. It provided a brief understanding of the topics, but I do believe deeper examples might be helpful.

better than the first course since it involved breaking into new stuff w.r.t the Stanford Machine Learning course.. However, altogether not yet challenging enough to give 5 stars

Really interested to go deeper into this matter..

Interesting material but a bit complex to follow all the equation derivation. Need to repeatedly watching the video to understand the content. After learning this the hyper parameter setting in the ML setup is clearer to me.

I would like to have harder programming assignment, perhaps optional. Because instead of understanding the problem I just had to read the question again, answer were always in questions, then copy paste and change "x" by "X"

Overall, it's pretty good. I did have a problem understanding some of the facts being communicated about gamma and beta in batch norm. Also, I think there is a problem with the last notebook. My cost did not go down as fast.

I think the last subject, the "gradient checking" looks a bit not connected to the begining of the week 2 course, it may be only me, so I will go over it again and try to understand its relevancy to the rest of week 2.

Teddy