Jul 01, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
Oct 30, 2018
The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
por Simon R•
May 13, 2018
Loved the course, Andrew is a great teacher; very impressive ability to explain and give intuition. I can really see how I can build upon this course to help me in what I am doing at work. I think there is definitely some room to go deeper on some of the topics e.g. don't just teach sequence to sequence but also broader uses of recurrent networks. Maybe a follow-up course? ... please???
por Fabrice L•
Jul 18, 2018
This module of the specialization is a bit more complicated than the others; at least to me, I found the concepts more difficult to grab.
Anyway, thank to Andrew and his team for this amazing specialization. The lectures are great, the assignments are fun and have interesting examples. A huge amount of knowledge along all the courses. You can tell there is a lot of work behind it.
por Apoorv V•
Jan 03, 2020
I was about to give this course a 3-star rating unlike the other courses in the specialization, which I have rated 5 stars. The reason for that was the programming exercises in week 1. They are not interesting and do not impart a lot of learning. Please consider improving those. The reason I still gave 5 stars is because of the amazing programming exercises in weeks 2 & 3. Thank you.
por JC Q•
Feb 10, 2018
In the continuity of the 4 previous modules, the Sequence Models course is of very high quality, the material is concise but cover a wide range of applications and methods, and is delivered with consistent clarity. The programming assignment gives very good hands challenges. I highly recommend this course to anyone interested in natural language processing or speech recognition.
por Mashrur M•
Jun 30, 2019
This is the last course of the Deep Learning journey, and I felt like a learned a lot in it. Sequence Modelling is a different beast compared to non-time series models, but I've mastered it thanks to this course nevertheless. I would recommend this particular course to anyone who has a moderate understanding of deep learning and wants to get into time series analysis and nlp.
por Peter V•
Sep 12, 2018
A succinct overview of a number of ideas in sequence models. Some of these were covered in an NYU course I took 4 years ago (embeddings, LSTM), others I had heard about but hadn't had a chance to look into (attention). The assignments were set up to be pretty easy, but I think trying to do them from scratch rather than by filling in code would make for a pretty good project.
por Guy M•
Sep 05, 2018
Great introduction to sequence models/RNNs. The real-world examples were very illuminating. Again, as with the previous course in the specialization, I felt some details of how to run/predict NNs using keras were lacking, which could leave a student floundering if they've never used keras before. This is in contrast to some other, much easier, tasks where hints were given.
por Alexander G•
Feb 25, 2019
Out of 5 courses offered I think this was the most exciting one as it combines everything learnt so far and teaches how to combine different NN modeling techniques to achieve desired classification/prediction features . For example, it is pretty much clear how one would go about building an app that would describe a picture to blind people and do that in many languages.
por Kévin S•
Jul 31, 2018
This short 3 weeks courses will make you work a little, exercice take at least twice the time write. You will learn about the famous LSTM, and how to use it on various tasks.
I'm not sure the 'translation' tasks is a good example but there is lot about it. Not a good example, because it is not state of art, and in the 'translation' business there place only for the best.
por Yogeshwar S•
Apr 01, 2018
This is a great course, and a great specialization. The professor explains the concepts across very well, not only in this course but in all courses of the specialization. My only gripe is with the notebook/hub/grading system which especially in this course has acted strange and cost a lot of time. That said I've learnt a lot, and am quite happy with the course content.
por SUJITH V•
Nov 13, 2018
Great course overall to learn the basics of sequence models and also get a brief understanding of the state of the art architectures used currently. The programming assignment on trigger word detection gives an insight into the practical machine learning implementation for speech recognition. This course combines both theory and practical advice in a very good fashion.
por Meynardo J•
Feb 06, 2018
Excellent course - and specialization! Andrew Ng's special talent is in being able to explain complex and difficult stuff with such clarity that you can actually understand it and follow. I found the exercises in this course tougher than in the previous four, but they were varied, useful, and FUN! Highly recommended to all who what to learn the "deep" in Deep Learning!
Jul 03, 2018
Because my research direction is NLP, so I think this course is very good for me. I learn how to implement the Sequence model such as machine translation 、Attention and so on. But the disadvantage is that there is no whole sequence model process. For me, The bigger problem is data processing and model. overall ,This course is good for me, I learned many from this.
por Matías P B O•
Sep 10, 2018
The whole specialization is excellent. I highly recommend doing it. The only minor comment that I add is that Andrew should include some links for further reading (along the courses for each topic or theme). I know that anyone can quickly look up for them in the Internet (and they can be outdated) but that would make the specialization even better in my opinion.
por José D•
Nov 06, 2019
This is Course 5 of the Deep Learning Specialization, and the last one. We learn Recurrent Neural Network (RNN) /Sequence Model, which allow translation or trigger word (like "Hey Siri!"). It's a completely different beast than CNN seen in Course 4. Again, nice videos and explanations, and well-designed useful programming assignment (TensorFlow/Keras and numpy)
por Hardik A•
Mar 05, 2019
The kind of simplicity with which the course is explained and the amount of knowledge gained is worth the hard work of many months! . . I would like to thank the whole Coursera team and Sir Andrew Ng for creating such a wonderful platform and a big shout-out to all the mentors and community who have replied to the most basic queries in the discussion section.
por Bing H•
Jul 28, 2019
Home work assignment notebook has a quick time out issue. It's quite annoying since most of the assignment has lots reading material to fully understand the topic or method. Often the notebook just time out and has to be restarted before move to the next section. Hopefully, it can be fixed or improved.
Overall a very good course to learn about sequence model.
por Jiandong S•
Mar 09, 2019
It took me a longer time to finish the course due to busy schedule. But I found it totally worth the struggle to work on the course material. Sequence modeling and processing is an new area to me. The course taught me some basic concepts and gave me a chance to lay hands on through programming exercises. I feel a lot like the topic particularly NLP. Thanks!
por Quentin G•
Aug 14, 2018
Très largement plus difficile que tout ce qu'il y a eu auparavant. Un véritable apprentissage et un plaisir surtout sur le Trigger Word Detection qui était très intéressant.
Totally more difficult than anything before. I've acquired true knowledge that I'm very proud of. It was a pleasure, especially for the Trigger Word Detection which was very interesting.
Feb 17, 2018
Exercises too basic. Better to do something simpler from scratch, them fill in the blanks on statements like "define a to be a boolean variable initialized to True", and run a bunch of other cells with imported code that hides the actual complexity. These kind of exercises does not add anything to the student. Content of Andrew's lessons is great, however.
por Gaurav K•
Mar 23, 2018
Thank you Prof Andrew Ng for sharing the knowledge and experience. It has been truly a great learning during the course specialization. And I always admire the way you structure the course and teach the advanced concepts with such an ease. With the power of AI, we as a community try to solve real-world challenges for better life. Thank you so much!
por Shifeng X•
Apr 07, 2018
awesome! Thanks to Andrew and his time to deliver this wonderful course. It really give me a very good sense about what's going on with the Deep Learning in several areas. The course material is prepared in a way that it's very easy to catch up. Just one suggestion, this sequence model session is too short, lots of topics haven't got well deployed.
por Martin B•
Mar 22, 2019
Very very interesting but it's a lot more difficult than the other courses. I did it out of sequence (no pun intended) (i.e. I skipped Convolutional networks to go straight to RNN, because they interest me more).
That wasn't the correct move. I barely knew anything about Keras, the exercises took a LOT more time. Still, it's a wonderful course.
por Wei H•
Jun 10, 2018
Great lectures on the intuitions behind RNN and their applications in real life. It requires some self-exploration to complete the programming assignments related to Keras and tensor, but the structure of the assignment is very good. I have learned a lot from this lecture and it helped me to understand the language of the field of deep learning.
por Alam N•
Jan 27, 2019
Finally, after 6 months achieved desired goal in Deep Learning Specialization.
Thanks to #andrewng #coursera team for motivation and opportunity. It was not possible but due to #andrewng motivational words and explained deep knowledge in simple words. I can't express my feeling in words. Thanks again
Best wishes for Andrew Ng and Coursera Team