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.
I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!
By Salman A•
This course has helped me in developing an understanding for implementing sequence models through Recurrent Neural Networks that can be used in number of applications such as Natural Language Processing and Audio detection.
A briefly introduction of Sequence Models to solve sequence problem, such as translation, speech reorganization..etc. Homework is also very helpful to understand what is going on step by step under Recurrent Neural Network.
By Moses W W•
This is an excellence training course! I had a wonderful experience learning the leading edge Artificial Intelligence knowledge specialized with Deep Learning and believe this will make a life-long impact to my career path!
By Gurprem S•
Excellent Course! The maths and concepts were a bit tough to understand and I had to look up some(a lot) of stuff but the learning experience and the thrill of actualling building and training the model is very satisfying
By BA M•
My favorite by far, and I'm not a fan on NLP. Sequence Models, especially attention mechanisms seem to have so much potential. Interested in using them to look at time series data analytics for industrial iot applications.
By Sorin G•
Excellent Course by Professor Andrew NG, I enjoyed learning what lays under the concept of Deep Learning and Neural Networks.
Thank you very much to Andrew Bg and the team, and as well the mentors supporting the students.
By Junfei S•
The course content is great overall! The only thing I am a little unhappy is that one or two of the programming exercises have confusion instructions. But finally I made it under the help of peers on the discussion forum.
By T T•
RNN model was quite difficult for me to learn, but all these lecture videos and programming assignments helped me understand it better. I liked the "Trigger word detection" (the last assignment of this course) very much.
By Shahin Z•
Absolutely fantastic course! Perfectly follows on from the Machine Learning course by Prof Ng et al.
(One slight issue with some videos' audio: there was a very high-pitch whistling that was almost painful to the ear.)
By Joakim P H•
At first I thought this was the least interesting course, but after the lectures and labs I have to say that this is really the most interesting of them all. However it requires some knowledge from the previous courses.
By Shankar G•
The final course was very brief and bit harder to digest. The assignments and quiz where also tricky but, overall had fun. Thanks Andrew Ng and team for the Deep Learning Specialization course to be offered on Coursera.
By Michał K•
Out of all five specialization courses, this was second most useful (right after first course in the series). Also one of the few that used any modern DL framework (Keras) and not implementing pseudo solutions in numpy.
By Rúben G•
I was able to understand the difference between sequence models and previous course models. Moreover, I understood now how text and speech can be processed by AI. Finally, I could understand better the Keras framework.
By Abishek S•
This was an excellent course. The materials were perfectly structured to maximize understanding. I had no idea of a RNN and the course made a fantastic job in explaining and helping me develop a precise understanding.
By Arun K S N•
Yet again Andrew Ng explains complex topics related to Sequential models in a much easier and understandable way . Barring few problems with the assignments and missing overview on Keras , overall course is a good one
This Is very helpful course in order to learn Recurrent Neural Networks. The first 2 weeks were amazing but the third week was a bit less interesting. This whole series of Deep Learning Specialisation is really good.
By Asaduddin A Z•
firstly, I know about RNN is from this course, the explanation is clear, combination between theory and practical is great. This is a good resource for you if you want to know about RNN, NLP with Deep Neural Network.
Thank you! Although I have been working on AI for more than a year, coursera has given me a more systematic understanding of my previous work, and many opinions have been very helpful to my work, thank you very much!
By Ankit S•
This course was very good in terms of practical knowledge as it will keep challenging you in each and every assignment. It will make sure you utilize discussion forum thoroughly :) I loved it Course 4 and 5 are best.
By Gema P•
This course is excellent .
It might be pretty intense of an ending specialization course .
It might be extended with how to structure sequence machine learning projects module .
Thanks again for making this possible !
By Fabian M•
Thank you for creating a course that provides so many insights into such a difficult topic. Were it not for this entire specialization I might still be lost looking for a way to enter the field of AI. Thanks a lot!
Andrew Ng's course is immensely enjoyable and accessible as usual. I especially appreciated the use of durian as an example throughout. It very much made me hungry and nostalgic for my time at home in Southeast Asia.
By Madhuri S J•
The Sequence Models course was very unique. When I had read about RNNs many concepts were not clear. Now I have a better understanding of RNN, GRU and LSTM. But I have to still learn dig more into Attention models.
By Joachim D•
Great course, very clear presentation and interesting examples on how to prepare data and how to use keras. Very interesting on how easy it is to build & train complex networks with keras (once you have the data..)
By Ankit V•
Andrew Ng Sir made deep learning so easy to understand and interesting. I was intimidated by words LSTM ,RNN, Attention etc but now i am comfortable with these concepts. Thanks a Lot Sir for this wonderful series.