30 июня 2019 г.
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.
29 окт. 2018 г.
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.
автор: Jianxu S•
18 июня 2020 г.
With time and perseverance, most of us are able to complete this final course of a rather challenging specialization. I particularly like the final course because the programming assignments combine architectures and techniques we learned in previous courses/weeks including CNN, RNN, GRU, Attention, LSTM, just to name a few. We also repeatedly write codes in Keras which give us a lot of practice and without being bogged down to every little detail. Big thanks to Andrew and team for making this specialization available to world's deep learning community.
автор: Jairo J P H•
1 февр. 2020 г.
El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!
The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!
автор: Marcel M•
27 июля 2018 г.
This is a superb module which provides you with the skills that will enable you get going fast in developing real world applications that can be modeled as sequence data. You learn of the latest state of the art techniques of developing sequence models using techniques such as GRU's, LSTM's, how to debug them and also how to employ Attention models to make your models that much efficient for problems in NLP, Machine Translation and Speech Recognition. This course is a must for anyone who wants to be a sound practitioner of AI. I love it.
автор: Sikang B•
1 апр. 2018 г.
Though there are some minor lost clarifications in the flow, the general learning experience of this course is overwhelmingly practical and relevant to many real world scenarios. Personally felt this course completed the knowledge graph (of course I only have a preliminary understanding of everything) and opens many doors for future learning.
One nit-pick is Keras documentation can be annoying confusing and misleading at times. Would suggest to revise programming assignment instructions based on some popular threads in Forum discussions.
автор: Aleš D•
4 мар. 2018 г.
As usual, Andrew makes AI almost look easy. I have one comment about programming exercises. There are errors in the text sometimes and, at least personally, I don't have a habit to check discussion forums first, before starting work on the assignment so these things were sometimes a source of lost time, scratching my head where have I gone wrong only to find that the results are correct and it was the notebook that was not up to date.
This aside, I would recommend this course to anyone interested in AI. Keep up the good work!
автор: Ali S•
5 янв. 2019 г.
Finally, I understood LSTMs, thanks to this course, thanks to Andrew! Before this course, I spent many hours reading papers on LSTMs and trying to figure out what is going on with all these "Gates", but couldn't understand intuitions behind them. In this course not only I learned and understood them, but also I learned a lot about machine translation and speech recognition which I was frightened to approach them. This course gave me all fundamental concepts and tools that I needed to be able to deal with sequential data.
автор: Alina P•
23 нояб. 2018 г.
Completed Deep Learning specialization in the DeepLearning.ie. I really liked this course, it will be useful not only for the beginners, but also for the specialists, which want to have an overview about current neural networks trends and see the interview from the best specialists of AI. To make this course perfect I would recommend to fix some errors in the theory of programming assignments (specially in the last 2 courses). Sometimes this issues are confusing and forcing to check on the forums correctness of the task.
автор: Roni M•
22 апр. 2020 г.
The material was interesting and very clear (like previous courses in this specialisation)
I think due to the complexity and nature of these subject, it's hard to grasp it all based on this programming assignment because in each exercise I was only able to implement a fraction of the "big picture". It would be very helpful to have a kind of "running" assignment, in which you start with an actual blank slate, and build all the building blocks from scratch so I can have much better understanding of the bigger picture.
автор: Long C•
3 июля 2020 г.
Lectures are great as expected. Thanks Andrew for everything! Programming practices are so good that everyone can see the designers must have put tons of hours to prepare for learners. Especially all the steps about the GRU, LSTM, Attention and etc are listed very clearly with no confusion. As of today as far as I know this course must be The Best online course about Sequence Model. Will keep learning from going over all the materials it provides. Debugging is a part of learning though painful and time consuming.
автор: Kai-Peter M•
28 окт. 2019 г.
Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that specialization, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable. The only thing I would wish for future participants: please make it easier to get the complete Jupyter notebook environments from the Coursera platform once completed. I spent a lot of time here - even after consuming the related blogs.
автор: Tian Q•
6 янв. 2019 г.
Great content! Andrew's lectures are great as always. The assignments are absolutely exciting and fun. Obviously the team put a tremendous effort on the programming exercises to make them doable for laymen yet not trivial. The exercises avoid using libraries (like Keras and TensorFlow) at the very beginning. Instead, they started with the more basic Numpy implementations. After these practice, I am able to grasp what each layer is actually doing.
My only suggestion is to correct some trivial typos in the Notebook.
автор: Marc S O•
13 сент. 2020 г.
I like how it injects the idea of a subjective language/audio processing into a mathematical model learned by a computer. The whole team managing this class has been very careful in setting up the quizzes and machine problems to let the students get their hands dirty on machine learning projects. I gave 5 stars because it encourages people to read research papers, tells if a particular paper is hard to read, recommends papers that are easy to read. This way, more people can be interested in the world of research.
автор: James D M•
13 февр. 2018 г.
Thank you for helping me to get over the initial barrier to entry in NLP and audio data with this Sequence Models course. LSTM's are core to so many current technologies, and building them from scratch has provided me with good intuition for working with them. There was a good mix of numpy and Keras, as well as having the homework be clear enough to work through without getting stuck on minutia. It's always a pleasure to listen to Andrew Ng walk us through a problem with clarity, simplicity, and enthusiasm.
автор: Anders A•
4 апр. 2019 г.
The course is well thought and easy to follow. I regret not starting on this earlier in my quest to understand RNNs. It is the best source I found through shopping around. The courses is scheduled for three weeks, but is actually doable in an afternoon + a morning session if you have some python programming skills and enjoy 2x on your lectures. My one complaint is not with the course itself but the whole series. I mislike the subscription model for payments. I prefer a one time payment for life-time access.
автор: Maximiliano B•
2 янв. 2020 г.
In this last module of the specialization, you will learn in details how the recurrent neural networks works. I really enjoyed and had fun with the programing assignments specially the Emojify and the trigger word detection. After the course, you feel comfortable to read all papers mentioned as references throughout the course. Moreover, professor Andrew NG is awesome because he explains the content clearly, it is a pleasure to watch his videos and he provides everything you need to go the extra mile.
автор: tarun b•
3 мар. 2018 г.
Couldn't be more grateful for having the opportunity to take this specialization. The instructions were just at the right level of illustrating theory in practice, and the programming exercise at the right level to gain intuitions with implementation details. So many rights !!! Personally, I had the confidence that the syllabus is exhaustive and the callouts to research were just great. Overall ... excellent resource I will revisit often. Thank you to everyone who put this together and to Prof Ng.
автор: Vignesh S•
22 окт. 2019 г.
Thank you, everyone, on the team for such an orchestration of the course. It was excellent to get to know the concepts of deep learning and it increased my interest in the field exponentially. A special thanks to Dr. Andrew NG for those explanations given in detail. This course was really interesting and it definitely overturned my attitude towards NLP as at first, I thought this is gonna be a difficult field of AI.
PS: Keep that ever-smiling face of yours the same Andrew Sir. Thanks a lot.
автор: David T•
24 июня 2020 г.
Very effective mix of theory and practical examples, cemented by practice exercises in form of the programming assignments. The guided instructions of the programming assignments are as valuable as the lectures. I would recommend putting the "errata" readings *before* the videos containing the errors (as was done in an earlier course). I also notice that some quiz questions pick up on nuances that were quite underplayed in the lectures, but going back over my notes, did find them.
автор: Amey N•
15 дек. 2019 г.
Smooth and hands-on walkthrough of basics of NLP and speech recognition. The flow of the course is very well-designed.
After having completed this specialization I can confidently say that I have a much better understanding of Deep Learning than what I had before I underwent the specialization. This includes the depth and breadth of DL, various models, their challenges, advantages & disadvantages, end-to-end pipelines, optimization techniques, background calculus & math, et cetera...
автор: Brian H•
21 мар. 2020 г.
Amazing course overall. Prof Ng's diagrams are the clearest explanations of DL models I have found anywhere and it's that clear a ton of thought went into planning the notations. The assignments are exciting and surprisingly fun. One could say that there is a little too much handholding throughout the assignments, but I understand that this course is more about the heuristics. Again, it's fantastic course overall and the resources provided throughout are truly unique to Coursera!
автор: Kuntal C•
20 окт. 2018 г.
This was my first AI course and I really made significant progress in my understanding of foundations of deep learning with this. Thanks to Professor Andrew's very informative course videos, grasping the complex concepts became possible. The quizzes and the assignments were challenging, made possible for me to use logic and develop new coding skills to go at it. I would recommend this course to everyone interested in AI/ML. Thanks to Professor Andrew for making this course.
автор: Mohd F•
23 июля 2019 г.
This is an amazing course, it Provides a great Help...i have learned lots n lots of stuff about NLP, Learn about recurrent neural networks that work extremely well on temporal data, word vector representations and embedding layers --that are explained in a concise manner, and more importantly I love the Attention mechanism, the model that understand where it should focus...... its attention given a sequence of inputs.... amazing amazing ..highly Recommended.... Thankyou
9 янв. 2019 г.
This course was a great introduction to the world of RNNs. Starting from basic sequence models all the way through RNNs constructed with Convolutional layers, LTSM layers, GRU layers and wrapping up with the Attention Algorithm. It is great base work to start a Deep Learning career. The course is very well structured and the resources in the forums were always life-saving. Very grateful for this course and I am waiting for the Advanced Specialization from Deeplearning.ai
автор: Janith G•
9 нояб. 2019 г.
Really good course for RNNs with NLP. Recommended to anyone who has completed the first four courses of the specialization. A thing to notice is that the last programming assignment is really hard to save and submit to your servers though it was pretty well organized.
Also I would like to thanks Coursera and Prof. Andrew for bringing ML DL and AI to a level that a student can understand without any useless long mathematical proofs. Thank you for giving this opportunity.
автор: Artem D•
12 июня 2019 г.
I really liked the whole Specialization, it is great: clear and interesting!
But the last course seemed very difficult to me: may be I've been pretty overhelmed (I've completed the spec in less then in a month), may the topics are much harder then in previous course, may be Andrew Ng wanted to cover too much items in short time. It seemed to me hat CV course was more clear.
Nevertheless I rate this course @5 stars and beleive that the spec is PERFECT!
THANK YOU, ANDREW!