RNN, LSTM, GRU... fun stuff even you don't focus on NLP. As always, Andrew makes complicated things simpler. I certainly will keep all the course materials for future reference.

It may be easier to follow other online course, but this course will teach you not just how, but also why...

Read coding instructions carefully and pay attention to details, otherwise you may end up with hours of debugging. That's what happened on me, LOL.

Sequence Models are a though subject. many people, during working meeting, mention them as the final resource and solution to everything. I feel I better understand the nuances of them thanks to this course.

I personally enjoyed some of the extra reading ( original papers quoted at the bottom of the videos). Sometimes is hard to navigate in the large sea of publications. It is nice to be pointed towards some piece of reference

Thoroughly enjoyed this course. It's deep enough to provide a clear practical understanding of the concepts, while broad enough to cover actual topics that are of value in the industry today (like Transformers). Compared to the Machine Learning Specialization, the NN material in this course is far superior. The advantage of the ML Specialization is that it covers non-NN material, while this one focuses purely on NN solutions.

I go back and forth on whether the time-saving aspects of the Python Notebooks are worth the reduction in ML coding experience. I suppose these aren't coding classes, but I also feel some of the concepts aren't cemented as well as if the students were led through a more challenging, trial-and-error experience. That's hard to do, though.

Overall, I recommend the specialization. Maybe just be sure to play around offline, too. :)

Immensely grateful for holding this course, specially Prof. Ng. The way he explained the all concept to the mathematical models was very endearing and excellent.

That was great, however I was expecting to learn at least 1 allocated week to time series data and forecasting (prediction) in sequence model.

In addition, if in one assignment we had imported data ourselves, we could have learned the section much better.

Best Regards

The only trouble with this course is that we're talking about seriously deep networks. That means it's difficult to present working, practical cases (jupyter notebooks) to work all the steps.

Still, I'd recommend presenting more and simpler steps towards building an RNN (particularly an LSTM). I had to come back to the notebooks several times... and honestly, I think I'll get back there again to try and understand better...

This series of course provides a comprehensive overview of NLP algorithm and different applications. I really enjoy the projects the deal with audio files. The course skip the linear algebra and differentiation part that not everyone wants to look into. But I hope it will be better if we could also implement the data processing functions of different types of sequential inputs, since data preprocessing is also significant

My knowledge has been upgraded to the next level through Coursera's Deep Learning Specialization. Through systematic and easy-to-understand explanations, quizzes, and program exercises, I was able to increase my interest and understanding of Deep Learning. Based on this, I really want to change my field of work and work related to Deep Learning. My goal is to make myself a person who uses AI without being replaced by AI.

This was a great intro to RNNs and Sequence Models.

Particularly liked the assignment on voice keyword detection. It was useful to learn how to synthesize a dataset quickly and train a proper model for the task.

Also the NLP parts were useful. I would have liked to have more advanced assignments, but still it was a great course that gives you enough knowledge to learn more on your own or explore more advanced courses.

I had struggled with the complexity of Sequence Models ever since I started learning about Machine Learning models. This course gave me an easier intuition to the sequence models without dwelling too deep into the mathematical complexities. As a person who has very little experience with Linear Algebra this helped me a lot to understand and apply such architectures to solve problem. Thanks Prof Andrew and the team! :)

I think it is a great course. There are some issues here and there with notebooks and related materials. However, considering the large and detailed amount of content in this course and it being a new course, things not being 100% perfect is OK by me. I would rather have the thoughtful content and exercises, versus something much lighter that would be easier to produce. Thank you to all who prepare these courses.

Absolutely fantastic. I am Deep Learning professional focusing on Computer Vision and had worked in Vision all my life. I started learning multi-modal AI, and found that my foundations on Sequence Models was shaky. So I came here and learned much about them. Now I can continue my journey towards muti-modal AI such as CNN-RNN hybrid models for image captioning. Such lucid explanations! Andrew Ng is a great teacher.

Superb course structure, the assignments beautifully complement the lectures and the amount of guidance makes it easy even for someone not too acquainted with programming. As a suggestion would have liked slightly organized detailed presentations which would help in reviewing the course material later by glancing through rather than going through the lectures. Over all an awesome course with great learning. Thanks

I enjoyed learning all the five courses of this deep learning specialization. Special thanks should go to Dr. Andrew and the instructors for delivering the course material in an interesting manner. Quite frankly I'm a little bit disappointed with this specialization being too short. Expect more courses in this specialization in the future. (Maybe reinforced learning).Again thank you all for this great experience.

I'm grateful for the chance to take the 5 courses in this program for a very affordable price. It is the best educational deal I've ever come across. The courses are well taught, I will continue on to take other courses offered online on the same subject. Thanks to everyone who made this possible, and I will definitely try to make a contribution to humanity as Prof. Ng has challenged us to do.

Thanks again!

Pretty interesting course, learned a lot about natural language processing using differen approaches for different input types. There were some challenges with completing the assignments, but they're managable by checking out the forums for help. Although I might not fully grasp the content material or algorithms, I still learned a lot or at least gain some familiarity through the lectures and assignments.

Thank you Prof. Andrew Ng and team for these series of courses. The entire specialization was brilliant and the way the programming exercises were structured, using real-life examples was the best part of all. Prof. Andrew Ng always has a smile on his face when he explains the concepts and he is so humble that he thanks us for spending time doing the specialization, whereas it should be the other way round.

I highly recommend this course to all audience. Professor Ng is an outstanding researcher with tremendous amount of experience. Moreover, he is a well-known lecturer in terms of his clear explanation and interesting examples provided in class. I have gained a lot of experience as well as knowledge in the field of deep learning. I am very grateful for his time and effort for providing all the resources here.

Amazing, challenging, and exhilarating. It involved a significant amount of effort, complex models, and ultimately, everything boiled down to numbers. Without a background in TensorFlow, it would have been incredibly challenging.What helped a lot - the Python knowledge and reading the code and the TensorFlow documentation.

I'm grateful for the journey and the lectures provided throughout this adventure.

The best online course I've seen anywhere about recurrent neural networks! Prof. Andrew Ng explains everything in such a simple manner. For example, understanding the structure of LSTMs is quite challenging, but Prof. Andrew Ng explains it in a very easy to understand fashion. Likewise with GRUs, Seq2Seq models, bidirectional RNNs, etc. And the code exercises have very beautiful and detailed explanations.

Thanks Professor Andrew Ng and team for the deep learning specialization. The course material was well designed for online learning. The assignments were perfectly manageable with a few hours of investment every week and the learning was very effective. Last but not least, I found Professor Ng's wisdom, insights, tips to be invaluable to anyone regardless of their level of expertise in machine learning.

This was the best course among 5 course specialization. It was well designed, structured and application oriented. Assignments were pretty fun to solve as they involved solving real world problems. This course gave me direct exposure to industry level problems and helped me gain more insights towards the future of deep learning. Because of this I am really excited to continue working in deep learning.

Hello Andrew, I am a big fan of you. Learning from your every course. Very unfortunate that I can do that remotely only.

One thing that I want to mention - Can we have lecture notes on coursera, just like the way used to in CS229 that we can read before coming to next lecture. I found that that was very useful in understanding when things get harder.

Thanks hope we can improve coursera in that matter.

The Sequence Models course covers state-of-the-art deep learning methodology as of 2018. The instructor is awesome. The assignments help solidify concepts presented in lecture videos. One nitpicking comment. This course, being relatively new, was less polished compared to the other courses in Deep Learning Specialization. I'm sure future updates will eliminate glitches and errors in the near future.

Good course to help you understand how sequence models work and how to apply them for various problems. Majority of topics are explained quite well. Practical problems sometimes could be a challenge, but every problem has hints and a bit of theory provided. Overall this course was a very positive experience and I do recommend it. Special thanks for the people who made this course possible.