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.
Jan 02, 2020
Learnt a lot about new concepts in RNN and LSTM. Really wanted to learn about these models. This course helped a lot. Everything was new and so fascinating. Loved this course and our teach Andrew NG.
por Vignesh S•
Oct 22, 2019
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.
por Amey N•
Dec 15, 2019
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...
por Brian H•
Mar 21, 2020
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!
por Kuntal C•
Oct 20, 2018
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.
por MOHD F•
Jul 23, 2019
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
por Alejandro R•
Jan 09, 2019
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
por Janith G•
Nov 09, 2019
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.
por Artem D•
Jun 12, 2019
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!
por Carlos V•
Feb 15, 2018
Another Excellent Course from Professor Andrew Ng. The detail in the explanations are excellent, and the provided exercises using Jupyter are super fun to complete and put to the test your knowledge offering you at the same time a library of ideas and models to use in your future projects. I enjoyed this last course in the specialization quite a lot, thanks very much to Andrew Ng and the Staff from Coursera. I hope to see more courses like this in the future.
por Prithvi J•
Mar 05, 2020
A greatly knowledgeable course! I learned a lot about Natural Language Processing and explored RNNs, LSTMs, Word Embeddings, Seq2Seq Models, Attention Mechanism, etc. The course focuses more on the concepts along with providing the essential math. It was fun to implement Language Models, Neural Machine Translation & Speech Recognition. I would surely recommend this course to the ones who are diving into the world of NLP, and need a perfect introduction to it.
por Huanglei P•
Jul 31, 2018
This end course is a little more complicated than the previous ones, especially in programming homework. However, it also inherits the merits of the special, gives learners the basic framework of sequence models. What impresses me most is the lesson of "Debiasing word embeddings", it shows that AI could be designed to do more against human stale thoughts, which sets up a good principle for designing AI. Yes, it should be taught to new learners of AI.
por Andrés G D•
Mar 22, 2020
Finally... Every piece of effort was worth it! After so many hours, now I understand how proud we can fell of completing these amazing courses! The best one I have tried so far, definitely made a difference in my professional views but above all, it confirmed my expectations: this is the activity sector where I want to develop, the work in which I want to grow without any doubt.
Thanks Andrew. Thanks Team. Thanks to everyone who made this possible.
por ANSHUMAN S•
Jun 25, 2019
This was the most difficult and most interesting course i had in all of the five deeplearning.ai courses
but after doing all the 7 assignments i feel like i learned a lot and encountered with some of the amazing thing which i wondered how they are done . Once again I thanks to Andrew Sir and other teachers for beautiful lectures and perfect quizzes assigments and at last a heartly congrats to Coursera for giving this platform to me.
por Mihai L•
Mar 21, 2018
Will give this course also 5 stars. The assignments were easy but required some knowledge of Keras. So you have to invest some time on their site.Otherwise it's like fitting pieces in a bigger puzzle. Most pieces are already layed out for you .. you need to just fit your small ones.
I realize though that deep learning requires a lot of practice and experimentation and completing this course (and specialization) is just a tiny first step ..
por P S R•
Feb 12, 2018
Course contents and coverage was best. Duration of 3 weeks is little too short to really understand all the details of programming exercises. May be extend this to 4 to 5 weeks and spend little more time on speech recognition, music generation and other audio data processing would have helped.
Unlike all other earlier modules, this one had many issues with grader and many errors in note book templates. Hope these will be addressed in future.
por James B•
May 02, 2018
Wonderful course, expert instruction from Prof. Ng. I can't recommend the Specialization enough.
The choices of architecture and of hyperparameters for the assignments' network could have used further explication. Another desire left unfulfilled was that I would want the sequence models course doubled in all dimensions, ie lectures, assignments, etc. It was all over too quickly with questions lingering. Further study required!
por Weinan L•
Apr 07, 2018
RNN, LSTM, GRU... fun stuff even you don't focus on NLP. As always, Andrew makes complicated things simpler. I certainly will keep all the course materials for future reference.
It may be easier to follow other online course, but this course will teach you not just how, but also why...
Read coding instructions carefully and pay attention to details, otherwise you may end up with hours of debugging. That's what happened on me, LOL.
por Chris D•
Jan 11, 2020
I go back and forth on whether the time-saving aspects of the Python Notebooks are worth the reduction in ML coding experience. I suppose these aren't coding classes, but I also feel some of the concepts aren't cemented as well as if the students were led through a more challenging, trial-and-error experience. That's hard to do, though.
Overall, I recommend the specialization. Maybe just be sure to play around offline, too. :)
Jul 01, 2019
This series of course provides a comprehensive overview of NLP algorithm and different applications. I really enjoy the projects the deal with audio files. The course skip the linear algebra and differentiation part that not everyone wants to look into. But I hope it will be better if we could also implement the data processing functions of different types of sequential inputs, since data preprocessing is also significant
por Stefano I•
Dec 12, 2019
This was a great intro to RNNs and Sequence Models.
Particularly liked the assignment on voice keyword detection. It was useful to learn how to synthesize a dataset quickly and train a proper model for the task.
Also the NLP parts were useful. I would have liked to have more advanced assignments, but still it was a great course that gives you enough knowledge to learn more on your own or explore more advanced courses.
por Najeeb K•
Aug 24, 2018
I had struggled with the complexity of Sequence Models ever since I started learning about Machine Learning models. This course gave me an easier intuition to the sequence models without dwelling too deep into the mathematical complexities. As a person who has very little experience with Linear Algebra this helped me a lot to understand and apply such architectures to solve problem. Thanks Prof Andrew and the team! :)
por Frank T•
Feb 20, 2018
I think it is a great course. There are some issues here and there with notebooks and related materials. However, considering the large and detailed amount of content in this course and it being a new course, things not being 100% perfect is OK by me. I would rather have the thoughtful content and exercises, versus something much lighter that would be easier to produce. Thank you to all who prepare these courses.
por Eagle Y•
Feb 05, 2018
I highly recommend this course to all audience. Professor Ng is an outstanding researcher with tremendous amount of experience. Moreover, he is a well-known lecturer in terms of his clear explanation and interesting examples provided in class. I have gained a lot of experience as well as knowledge in the field of deep learning. I am very grateful for his time and effort for providing all the resources here.
por Kostas H•
Nov 05, 2019
The best online course I've seen anywhere about recurrent neural networks! Prof. Andrew Ng explains everything in such a simple manner. For example, understanding the structure of LSTMs is quite challenging, but Prof. Andrew Ng explains it in a very easy to understand fashion. Likewise with GRUs, Seq2Seq models, bidirectional RNNs, etc. And the code exercises have very beautiful and detailed explanations.
por Guruprasad S•
Mar 05, 2018
Thanks Professor Andrew Ng and team for the deep learning specialization. The course material was well designed for online learning. The assignments were perfectly manageable with a few hours of investment every week and the learning was very effective. Last but not least, I found Professor Ng's wisdom, insights, tips to be invaluable to anyone regardless of their level of expertise in machine learning.