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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
26,718 ratings
3,158 reviews

About the Course

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career....

Top reviews

WK
Mar 13, 2018

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!

AM
Jun 30, 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.

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2426 - 2450 of 3,130 Reviews for Sequence Models

By Cristina B

Mar 4, 2018

Always a great course but I would expect to have more lessons on how to use Keras and Tensor Flow API in a better way for who needs to use them in real NLP applications. I still have some doubts on how to use them correctly (for example the use of time distributed layer in the last exercise 'trigger word detection' that we didn't use in the architecture for the exercise about attention mechanism)

By David C

Aug 23, 2018

I really enjoyed this course and learned a lot. The descriptions of GRUs and LSTMs were a little scant, however, and I found myself rewatching the videos trying to get my head around them. The course could be improved by going into a little more detail about the different gates and what it means to train them, or what sorts of information or patterns might be relevant for the training of a gate.

By Daniel C

Nov 13, 2020

Although I loved the course and learnt a lot, I don't feel as confident trying to implement some sort of sequence modelling in practice compared to the other courses. And yet I still got full marks for this section. I think the course could have been spread out to 4 weeks with a few extra examples (maybe some stock market prediction examples). Regardless, thank you so much for the teachings!

By Lida G

May 26, 2020

I really enjoyed learning this course and gained a lot of knowledge from it. The only challenge that I found was some of the steps of the assignments were not clear. I could resolve them by checking the forum. I would also like to know more about document summary and document similarity, but there was not much content for it. Overall, thanks a lot for putting this valuable content together.

By Anatoly R

Feb 18, 2018

Great material and amazing Andrew Ng (5 stars) but very pure editoral review (videos with a lot of repeats of canceled phrases, pauses, quiz understanding, grader problems, very poorness of mentors support because they can do nothing to help, neither contact deeplearning.ai, in summary it's looks like alpha version of course not release and diserve 3 or even 2 stars), so in total 4 stars.

By Vikram R

Apr 21, 2018

This course is almost as good as the prior four, but some of the lectures lack detail, there are mistakes in some quizzes, and the programming assignments at times are crammed too full of information. You can end up passing through this class without really understanding what's going on, whereas the CNN class does a much better job of forcing you to understand things before you pass.

By Daniel Z

Aug 14, 2018

Excellent lecture content.

Some of the programming assignments are quite poor. Sometimes there are minor mistakes in function descriptions, and other times the whole assignment architecture/plan is not well thought out. If the staff doesn't have resources to improve this, then allow the community to create branches and submit merge requests :)

Overall, I'm happy with this course.

By Paulo V

Jul 11, 2018

The lectures were great, making an advanced subject accessible. The course materials were mostly good -- the exception being the optional (non-graded) assignment in Week 1, which was not well-structured, and failed to reinforce the concepts it was intended to. There were challenges with connectivity to the Jupyter notebook server, which caused much frustration and wasted time.

By Christopher M

Aug 18, 2020

Another great course by Prof. Ng. The reason for 4 stars is that I found the assignments to gloss over a lot of new Keras ideas (for Keras beginners) at the expense of spending more time on how the ideas were being implemented. I think the course should be spread out over more weeks, say 5, and spend the extra time going into more depth around the Keras model architectures.

By Frank H

Feb 19, 2018

In the lecture videos there have been quite a few repetitions and in the programming exercises the necessary Keras background has not been delivered. For this I have to subtract one star.

The course's contents are very inspiring, challenging and interesting at the same time. I'm really looking forward to applying the techniques learned so far to problems in my business life.

By Nicolás A

Feb 18, 2018

The course could have covered topics like time-series modeling for prediction (sales, demand, a machine failure in a factory, etc) that is much more applicable than some of the assignments proposed here (half of them seemed to be just for fun). Also, I am a little dissapointed that the course didn't cover chatbots, which is one of the most widely used applications for RNNs.

By Dawar H

Mar 17, 2020

The course was nice but more mathematics could be taught in the lectures, especially backpropagation in recurrent network. Also I feel there could be one more week in this course where recent models like Transformers and BERT can be taught. Overall a nice course to get familiar with Word Embeddings, LSTM, GRU, and some other topics like Translation and Speech Recognition.

By Edward C

Feb 22, 2018

The discussion felt really complicated at points. Also I was disappointed not to be able to complete the optional assignment for LSTM back propagation. Since it is ungraded, it would have been nice to at least see the correct implementation to learn from. Also there were several errors in the expected values or instructions in the assignments, that were really confusing.

By Shringar K

Jul 28, 2019

The instructor Andrew Sir is excellent in conveying topics, but I just found the last part a bit dry compared to the previous 4.

And the course was a bit too long, even though it said 3 weeks.

But the hands on programming practices in this course, especially is second to none. Top Notch.

One would need to revisit and do it all over again to make it stay inside your head.

By Karl M

Mar 15, 2018

Ths course really shows cutting edge technology such as using deep networks consisting of LSTMs, GRUs etc.. I especially liked the audio trigger word recognition.

The translation with attention exercise is really much harder to understand than any other exercise from that specialization. I admit I have managed to implement it more using intuition than real understanding.

By P M K

Feb 23, 2018

It has been quite a good course to explain the tedious concepts of RNN.

The only reason for a 4 star is there is definitely quite some room to improve upon the content and quality to bring it up to the mark of the previous 4 courses. There are quite a few bugs in the assignments which need to be rectified for the benefit of everyone, hope that it shall be done soon!

By Matt C

Apr 23, 2020

Concur with other reviewers: this class was good, covering a lot of interesting material and with well-structured quizzes & assignments. But the lectures seemed to skip past the sorts of in-depth explanations I wanted, instead just getting to the end point of "this is what this looks like". So good, but not quite as good as previous courses in the specialization.

By Shikhar C

Feb 3, 2019

This course is great to get intuitive understanding of Word Embeddings, RNNs, LSTMs, GRUs and Attention Models.

You will have great explainer videos and some excellent programming exercises. The course does not make you an expert, but it does make you familiar with the above mentioned architectures, so you can independently code and try them on your own solutions.

By Duncan K M

Mar 31, 2018

Really cool applications to work on, but the videos got a little too much into specific applications that may not be relevant most of the time. It was all interesting, but it made this course a lot longer each week. I could have done without a lot of the specifics of certain applications, just because it will be hard to apply/remember the concepts anyways.

By Eric F

Sep 23, 2018

All courses in this specialization are awesome. However, this last course feels a little rushed in comparison with the other 4 courses. While the first 3 courses raise your knowledge of ANN in preparation to the 4th one, it is a little more difficult to understand this 5th course. Likewise, completing the assignments is possible, but more frustrating.

By Jörg J

Jun 21, 2020

Guys, just the truth: Content: Great. Mr. Ng: Great. Autograder: Complete and utter BS. If you rework the Infrastructure you will be big. If you further refuse to do so (literally thousands of complaints about the autograder in the forums -> nothing happens) you will not. Check out Scala courses approach with grading -> works like a charm. Cheers, JJ

By Robert P

Apr 16, 2018

The content is generally great and well worth it. I wish they would fix some of the errors, especially in ungraded exercises. You end up wasting a lot of time because of them. Perhaps the most frustrating aspect is navigating to the Jupyter notebooks. I wish the links to the notebooks were on the same pages as the Submission and Discussion links.

By Sung W K

Mar 2, 2019

I learned a lot. I would give 5 starts but the jupyter notebooks were very very buggy. I spent half of my time on the homework going through the forums to find workarounds. It took away from learning the material efficiently.

Note that I think that this may be a temporary problem as a new platform was release Jan 2019. The content was terrific.

By Elena B

Feb 13, 2018

The course is very interesting and it gives an insight into recurrent neural networks (RNN). The practical exercises are interesting but I found them in a bit raw state compared to the previous courses of the Deep Learning Specialization. Nevertheless I would still highly recommend to follow this course. Thanks a lot to organizers.

By Jungwon K

Feb 5, 2018

Everything seems logical, except the programming assignments. Although I went through week 1 programming assignments only, I often had to face some problems with insufficient information. Lecture videos are easy to understand, but not all the details are explained. (This is the point where I need to find some information by hand.)