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.
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 jaylen w•
Finally I finished the whole series of Deep Learning AI, through which I gained a lot of intuition of deep learning algorithms and its implementation. It's great course to get into this new era especially with a excellent teacher like Andrew who really illustrates the core ideas of deep learning algorithms to me.
por Pavel K•
The last module is awesome as all previous ones. Thank you all guys!
Thank you guys who posted questions, thank you guys who posted answers as well. I appreciate you all. And one more special appreciation to Andrew Ng for this entire course. This course gave me a great knowledge and intuition about Deep Learning.
por Rajan A•
I have been through wonderful journey of learning and implementing deep learning from very scratch. This course really transforms one from caterpillar to butterfly with very minimal pain of calculus and linear algebra. Thanks to Andrew and deeplearning.ai team for providing such a marvelous bundles of knowledge.
por Sardhendu M•
Lots and Lots of knowledge and experience in 3-weeks of class. In Machine Learning terms, this course maximizes the knowledge and experience gained with sequence models by minimizing the time required to complete the course. Lecture videos are very intuitive while assignment projects are very real-world centric.
por Saimur R A•
As always one of the best courses offer by coursera and it was a hell of a ride. I learned about many things like RNN,sequence model,GRU,LSTM,word triggered,word sampling,translation using deep learning algorithm . Andrew did a fantastic job and keep everything simple so that everything can be understandable.
por Matei I•
Really good choice of topics, including state of the art tools like attention and word embeddings. Very useful, especially for those interested in Language Processing applications. However, the videos and assignments need some more careful editing, because there are occasional mistakes, lazy explanations etc.
por Maryam H•
Very great and aspiring course, I learned lots of concepts in this course. I think It would be better If there was a capstone project for the final course of deep learning specialization. It was very great but If I had the opportunity of implementing a project from zero to 100, It would be more than great...
Great course, though not as awesome as other Ng's courses. I think creators became a bit tired closer to the last course in specialization. Anyway, fantastic courses, fantastic specialization, fantastic professor! Thank you very much!! Looking forward to new courses - maybe Reinforcement Learning, GANs? =)
por Shabie I•
Leave it up to Mr. Andrew Ng to explain complex concepts in a very much intuitive manner. The guy is a world apart when it comes to explaining complex concepts. No other MOOC even comes close. Absolutely highly recommended.
The only negative part about this course is that it ends. You don't want it to end.
por J.-F. R•
Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.
I loved this course as wel, I had a little bit less time to advance as steadily as I did with previous courses. So I had a little bit of trouble getting the context back when starting again each time. The next steps for me are putting what I learned into practice and basing myself on what I learned here.
por Pavel K•
This course offers a great introduction to the models: RNN, GRU and LSTM.
In addition, it illustrates the power of "Word Embedding" and "Attention Model".
The programming assignments are interesting, provide deeper understanding of the models, and show how simple it is to implement these models in Keras.
por Md A A A•
The course content is very good. This course seemed a little bit harder than other previous courses. There is lots of improvement opportunities for Coursera's Lab (Jypeter notebooks), every now and then it does not work i.e. cant submit the assignment, it's very very slow, its just not up to the mark.
por Akshay M P•
A very well organized introduction to the wonderland of Recurrent Neural Networks. The course takes the student through multiple use cases of various RNN architectures with practical advice on how and where to use them. The awesome programming assignments with excellent documentation is a highlight!
por Camilo G•
With this course it's possible to conclude that recurrent neural networks are the most powerful variant of neural networks. Used in a many places, this course expleains ingishtfully how they work, what's the math behind them, and how different implementations function for different areas of research.
por Angad S•
Andrew Ng knows how to make people understand the problem, architect a solution and then map it to data. He does it very well. I am going to repeat his course again as I still believe there is something that I have missed and I need to follow him thoroughly. I want to understand and process like him.
por Xiang J•
overall it is another great course, clear explain on the RNN, LSTM, GRU, etc. really like the assignment to implement RNN from scratch. assignments related to Keras needs "googling" outside resources, and there is still some keras homework to be done in order to fully understand the assignment code.
por Jampana b•
Thank you very much instructors. I learnt both fundamentals of deep learning and application of them in simple and efficient way. I have been long and fruitful journey with Andrew Ng. I learnt sound mathematics required for deep learning, tensorflow software, applications of sequence and RNN models.
Thank you very much for the course provided by the teacher. The original course experiment has relevant research needs for LSTM. This course provides the teaching of relevant knowledge points. Thank you again for the teacher and platform!
por TANVEER M•
I have always found difficult how RNN and LSTM works as theretically I was not getting a clear picture how it was working .The programming assignments helped clear my doubts and I got a clear understanding to a lot of extent how this mechanism is working and how it is useful in speech synthesis.
por Zhiming C•
This course introduces the basic idea of RNN, GRU and LSTM models. They are obviously harder than the CNN models and the concepts are not so easy to understand. Thanks to the systematic introduction! Together wit the excises I can understand better the theory from the applications. It's great!
por Andrei N•
The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.
por Nilesh K S•
It was a great experience to learn from Andrew NG and it helped a lot to me personally and professionally. I have gained so much confidence after completing these set of 5 courses and looking forward to build some cool projects on my own using the concepts that i have learned in past 5 months.
por Mihir T•
A great course on latest technology used in NLP. The course is well structured and provides an in-depth knowledge on sequence models. This course is a all-in-one package for starting your career in NLP. Mr. Andrew is a great teacher, and explains everything in a very simple yet effective way.
por Wesley H•
Great finish to the specialisation. I have learned a lot of the core details of how to proceed with my own Deep Learning projects. My one piece of feedback would be for an intermediary step, that requires more of the programming myself, as a lot of the intricate coding has already been done.