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.
brilliant course, great quality instruction from Andrew Ng. The only faults are that some of the labs have not been supervised properly being a but buggy and a couple of later lectures were very dry.
von 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.
von 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!
von 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
von 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.
von 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.
von 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.
von 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.
von 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.)
von 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.
von 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.
von 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.
von 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.
von 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.
von 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
von Robert Y S•
Fantastic course. I benefitted immensely from Professor Ng's easy-to-understand lecture videos and the practical programming assignments. Looking forward to taking more courses from Professor Ng and DeepLearning.AI!
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.
von 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!
von 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.
von 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 !
von 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.
von 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.
von 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..)