I would rate for this course 4.5, but Coursera's system does not have it.

About the first and second week, explanation about terms in Deep Learning are very good from Prof. Andrew, the preparation for exams is quite good for you to revise lectures. I think programming exercices should be more challenge and more suggestive for students, but it was okay for me after having some knowledge from Machine Learning Course. I suggest you to finish Machine Learning Course before taking this.

About the third week, i expect a lot more about TensorFlow that Mr.Andrew can give me, or maybe more intuiation about it. Moreover, Batch Norm 's explanation is quite hard to understand, because we do not have any programming exercise for it, so I hope teachers can prepare a programming exercises among with the programiing exercise for TensorFlow.

Having done multiple Udacity Nano Degrees and other deep-learning/AI courses on Coursera/edX, I can say that deeplearning specialization is probably the best and most detailed to master the basics of Neural Networks and deep learning. This course is great in helping understand tuning of hyper-params, various optimization techniques and approaches. Videos do a great job in explaining complex and confusing concepts in easy to understand style. Assignments cement the understanding further.

Kudos to the Prof Andres Ng and rest of the deeplearning.ai team for putting up such a great content.

This course provided me with an understanding of the large number of hyper parameters that have to be tuned during a deep learning project. It gave me an insight on when different techniques like regularization and (the many different forms of) optimization need to be applied. The only quibble I have is that the material on the choice of the number of layers and the number of hidden units per layer was thin. Given that these values have a great impact on the speed of progress in a deep learning project I would have liked to have seen a little more emphasis on them.

The concepts has been explained in a fantastic way. But few suggestions:

-> After every lesson, I would love to have more pop quizes. This was the case with course 1, but I didnot get any pop quizes for this one.

-> In the quiz assignment, it would be nice to have an explanation or justification section, which will explain that why the option selected is a correct one and why the other options are incorrect. I know we can have the same discussion in the forums, but such an explanation ( one liner should be fine) can provide a good instant knowledge boost!

I'm actually learning and comprehending the course, I do pause the videos occasionally to research some concepts, write some notes in a copybook but overall this specialty(so far course 1 & 2 ) is really filling the gaps in my mind to build a clearer picture of the topic of Machine Learning and Deep Learning. Andrew Ng explains really well, sometimes he through some good recommendations based in his practical experience and this is really valuable for me because it actually helps in improving the learning process.

Thank you Andrew and Coursera Team.

This course is perfect bite for your hunger of Deep learning. Before taking this course I have gone through some books and and some blogs too but there was not that much of clarity to topic so finally I tried for this one and trust me this course is so organised and very informatic so go for this one, I assure you will feel more confident and knowledgeable after completing this course. I would like to thanks Coursera as well as deeplearning.ai community for providing this course and Want to specially thanks to Mr. Andrew Ng for his contribution

This is the 2nd course from Andrew Ng in the "deep learning specialization". Having introduced the building blocks of deep neural networks, in this course Andrew teaches more advanced and practical concepts - like: regularization, advanced optimization techniques, batch-normalization, etc - that can significantly improve the implementation of the models we build.

Also, in this course we get to learn TensorFlow, a widely used and wonderful deep learning framework.

I highly recommend this course.

Thank you Andrew & Co. :-)

Great course! But I am not too sure why this should be placed in number 2, as I feel that topics such as tuning hyperparameters do not resonate well with someone who is not working professionally or is not very experienced in this field. However, still a great course as I will revisit this course when I gain more experience. I also like the last exercise on Tensorflow as there is a lack of courses on Tensorflow on the Internet, so the last assignment on Tensorflow is the most useful which I have found in the course.

Other than anything I've learned a great intuition about everything that Andrew Ng has presented in this course. Some I somewhat still feel like I still need to do some further readings and understanding because some of the concept from the course I still don't understand them. However, with the first course and this course of the deep learning specialization, I feel ready to work as a machine learning expert even if starting from bottom-up. I feel more than ready to finish the whole specialization certificate.

I liked that the course gradually introduces more and more complexity and concepts without making your drown. Even though existing frameworks (e.g. tensorflow) can be used so that most of the complexity is hidden it is still required to understand why one method should be used rather than the other. This course I believe addresses this (as well as first in this specialisation). I still wish there was more information on details but probably all that is needed are external links to extra material.

Fantastic course! You can experience short, easy to understand lectures, followed by plenty of opportunities to implement covered material, and most importantly create optimized image classifiers - like cats, dogs. I liked how up until the end of the course you had to implement everything from the scratch, not just using read-made frameworks. Finally, you are introduced into frameworks, but this deep understanding stays with you. 5/5 recommendation. Bye, I gotta finish the rest of specialization.

Good course explaining the concept of hyperparameters vs. parameters, how you can tune the hyperparameters, as well as different regularization techniques. It also provides good explanation for different optimization algorithms (enhancements to stochastic gradient descent). It is a highly recommended course for those who want to understand what is happening under the hood when using a neural network framework, like tensorflow. In the last week a brief introduction to tensorflow is also provided.

The course is great as I expected. It would be helpful if more mathematical background in videos or notes can be attached in each weeks. Moreover, more code could be given to us to write by ourselves to get much better intuition. Rest each of the specializations are awesome as was the first learning Andrew Ng course on Machine Learning. Thank you for providing such courses. This is a great deal for all such students who cannot afford to attend Ivy leagues due to their own reasons.

Great Work!!

It explain neural network from the start. After doing all the 5 courses on deeplearning, it is hard to remember normalization formula, and every details. Sometime some hyperparameter look like a little bullshit: You don't know how to do : add one hyperparameter and go for an argmax. But if it is how its work, then it is okay to learn it; Be ready to laugth and do not compare to pure methods like genetics or Bayesian programming that often work good. But every one should follow this course.

Insights into best practices and directions for common problems make it an one-of-a-kind material for learners. Andrew, as always, has been commendable with his tutor team, the exercises are well cleaned up and in good shape. May be, if some optional tough exercises are given, it will add more value.

The topics of this course, such as the setting of hyperparameters and the use of tensorflow, are critical topics for me, and in this course they are explained both in lectures and in practical tasks.

Very insightful. it would be nice, however, if the course had more information about Tensorflow 2.0.

体验很棒，喜欢这种有作业有评分的课程

very practical.

There are both areas needing improvement and places where this course excels.

To begin let's consider what I think needs improvement.

Since this program says that it does not require prerequisites, it really ought to provide backup reference materials specific to course content. Specifically I found it difficult to follow details without the basics of differential calculus, matrix algebra, Python, and TensorFlow. One alternative is to hire a tutor, which is what I did.

Although there is an active community and tutors, support from the course's owner deeplearning.ai can be improved. For example there are comments in the forums about how long it takes deeplearning.ai to fix bugs in the code.

My last suggestion for improvement is how this course is taught. There is so much content that questioning needs to be more frequent. Currently grading is done through ten multiple choice questions and a programming exercise after a week's worth of videos. While the programming exercises are good, learning would be improved significantly by including three or four questions with each video, even if they are not graded.

Overall, I have the sense that deeplearning.ai has not been improving or updating this course.

Nevertheless this course still deserves four stars. The presenter is well organized, articulate and enthusiastic. The entire course follows a coherent plan. This course and its predecessor supplies a great deal of content. Each video runs 6 - 10 minutes on average which is about the right length, However I was always stopping them to write down points to better grasp the content.

As mentioned, there are a few bugs in the programming exercises. However they are rigorous, cover the material, and effort has been made to make them interesting.

Overall, while there is room for improvement this is still a worthwhile course.

I enrolled in this course without taking the previous ones (I have already done an MSc in Computer Vision and Machine Learning so I thought I wouldn't need the others), but the material has been easy to follow and understand. It is really interesting as it helps you understand important concepts such as bias and variance, or why does batch normalisation work. Sometimes Deep Learning can be seen more as an art than a science, and this course is helpful for defining a good strategy when carrying out deep learing experiments.

The course began from very basics to complex functions, hyperparameter tuning is efficient in building better models, Kudos to Sir Andrew NG for explaining all of them in the simplest way possible. I would highly recommend this course to all interested in deep learning. But I believe that assignments can be made more challenging rather than just filling up the codes with syntaxes. Logic building is very important.

Add more programming assignments to clear fundamentals.

This is a nice but very basic introduction to the practice of DL (the last week about tf is nice). However, the assignments are way too shallow! In the assignments the students are "spoon-fed baby-food"... one can solve almost all exercises without thinking and without having understood anything (it is mostly solvable by copy&paste).

For instance, I have learned the most in the final assignment when it did not fully work and I forgot a tf.transpose(..) and I actually had to think about what was happening.

Anybody applying to our group who presents this course as evidence that they know about the contents will not be taken seriously (and rightfully so!) -- thanks for a very quick way to sort out useless applications (anybody presenting a certificate for this course in public).

The assignments could also be auto-graded by using the format of any programming competion (specifying the input-output relation, providing an input, and giving the student total freedom in how to implement the solution), e.g. like in the famous advent-of-code. Then the course would be harder (but way more valuable!) -- however, coursera won't get enough paying subscribers that way I assume.... oh what a pity.

Terrible construction of programming exercises. They either end up being extremely trivial or vert obfuscated. Sometimes too much information is given with no incentive to think or too little information is given leading to a deadlock. Week 3 of this course is utterly trash. Course content feels rushed and the programming exercise does not explain anything or clear any doubts. Why on earth do I have to do so little in these programming exercises. Why can't you make us write the little helper functions and plotters and the compiled model.