Un curso muy bueno, el instructor tiene dominio del tema y sobre todo el final del curso es muy bueno en cuestión de poner en practica la teoría que al principio te explica. En ocasiones el instructor va un poco rápido en los términos teóricos y puede llegar a abrumarte. Creo quería ideal poner más ejemplos prácticos cada que explica un concepto.

Materials are good and Professor Andrew presents the course in the really understandable level, so I still learn a lot throughout the course even if I have taken similar mooc courses on other platforms. Programming Assignments are much easier than the fourth course (Convolutional NN), but it gives many inspiration to me. Great thanks to the team!

Great material, very clear and pleasant teaching, good software environment for the programming exercises. The exercises are a bit boring at times (cut and paste without much thinking) but maybe this is a quick way to memorize the material...

Some long known problems in the exercises should REALLY REALLY be addressed! (would have given 5 stars)

Great course! The labs were very useful in seeing the concepts applied in practice. Something that I think would help all the concepts and practice take hold even more would be a second lab session per week with much less guidance, where the student is required to come up with most of the algorithm themselves. Overall great and solid course!

Content is great! A good introduction to a lot of hyper-parameters in neural net. However, there are some bugs in the evaluation system of programming assignments. For example, the system does not recognize Pythons '-=' operation and gave me a fail, which I did not figure out until I saw the forum where people were having the same trouble.

Much better than the first course. Math is still quite shallow (simple and not going into too much detail), and programming assignments are still mostly one-liners with copy&paste. But the general techniques demonstrated throughout the course are very helpful, and the given intuition about why and how something works helps understanding.

The course was very good. Things were implemented and taught well and at the correct pace. However, while completing the exercise, we can never write the whole code, we have to only edit a few lines of codes. That's not bad for a beginner, but it would have been better if a little understanding about that part of code could be given too.

Concepts are great and complex :) but the instructor is great at teaching complex things. The assignments weren't challenging or I can say they were too short and small for these lots of concepts: that's why I gave 4 stars. Materials In this module are not kinda continuous and that's why made this module difficult to teach and to learn.

Very interesting course, like all courses taught by Andrew Ng, who is an excellent teacher. The only thing I found strange was the TensorFlow introduction in the end. I think that it does not fit to the course topic. In addition to this, I found the introduction very restricted. But this is a tiny part of the overall very good course.

Andrew is great at teaching. Quality of education is absolutely for 5 stars, but I am giving 4 because of technical difficulties with Jupiter notebook. Often happened that I wrote some code and it could not save, it just displayed error, so I had to copy code to my notepad and rerun the Jupiter notebook, and than copy the code back.

I really like the content but I believe that it is about time the final assignment of this course is updated to Tensorflow 2. There is no point in enforcing learning outdated software... For the massive revenue that this course is generating, the minimal effort to update one Jupyter Notebook should not be too much of a burden...

The course was a very focused approach towards introducing and familiarizing us with the importance of tuning hyperparameters and their impact on the performance. Although, I personally feel like the Tensorflow exercise could have been more detailed and could have used more explanation. I found that exercise somewhat confusing.

The explanation is just as good as the previous course. The reason I give 4 star is that the notebook use TF version 1 instead of 2. Given syntax of 1 and 2 shows great difference, at least I believe so, it would be better that the notebook can be updated. For the rest of the course, very good!!! Suitable for beginners in DL.

This course is well taught.

Andrew Ng takes you through the material without error and in a very acceptable pace.

The exercises are very do-able.

They do not challenge hard, but take you by the hand and show you how to implement and improve your neural networks.

The final assignment is a very good tutorial on TensorFlow actually :)

Compared to previous module, this one was more of a cookbook and I expected more mathematics in terms of why each optimization work.

Overall, it was still a very interesting hands on approach, finishing with TensorFlow is a bit more difficult to apprehend as all the previous exercices were done in a very different way (Numpy).

The course content is great as always. It introduces all the concepts in consize manner. The final assignment however fairly rudimentary. Would have been more beneficial to me and perhaps other students, to go through writing the training code rather than having to figure out what tf.transpose or tf.nn.softmax functions are.

First of all thankyou to Prof Andrew and team. This course is really good. I learned a lot of new things. Week 1 & 2 programming assinments are really good but I was not able to understand well the tensorflow introduction assignment in week 3. Rest the knowledge given through this course2 of the specialization is very good.

Most of the course is great, good overview of different methods and techniques with practical examples. However the TensorFlow programming part is rather confusing, lacking in sufficient explanation of the syntax and overlapping names of python and tensorflow variables which end up producing near impossible to debug errors.

This is an amazing course which dwells into the nuances of fine tuning your neural network model. The content of the course is too good. Programming assignments was a bit off. It was really straightforward. Programming assignments could have been more challenging. This will make sure that the concepts are learned properly.

The content is generally great and helpful, but the grader did not show me why the result is incorrect, and i constantly had to reload jupyter notebook. I think there should be less template for the exercise so that we have more thinking to do, but the expected result should be maintained so that we know what we did wrong.

The course explains the reasons and intuition behind tuning hyperparameters and why/how regularization techniques work well when training on large data sets. The only reason I am giving this a 4 star is because the tensorflow introduction seems a little too sparse and could be done better.

Thanks again, team deeplearning.ai

The contents are actually good, and it doesn't require a very extensive prior knowledge, so it's even suitable for people with little experience in programming or math. However, despite being a course that has been out for over 2 years, there are still some subtitle issues (in English), and typos on a clarification slide.

Well explained..sometimes jumps a bit. I felt lost a couple of times. But I got through it and I'd say this is deifnitely one of the top courses out there.

If they included some optional videos on how this could relate to having a career in this area that'd be very helpful (i.e. what level we need to be able to code at).

This course continues to build on foundations from course 1 of the specialization. Hyperparameter tuning and Regularization methods are quite imperative for optimizing ML models. This course covers these concepts in addition to providing a good foundation for Tensorflow library. Overall, a good course by Prof. Andrew!

The course was very helpful as now I understand optimization techniques and all the parameters of neural networks. Unfortunately, the course has not answered my question how to tune the whole bunch of hyperparameters from the scratch, what is the correct order and logic of the full ANN tuning, not just one parameter.