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Comentários e feedback de alunos de Sequence Models da instituição deeplearning.ai

4.8
estrelas
21,458 classificações
2,445 avaliações

Sobre o curso

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content....

Melhores avaliações

WK

Mar 14, 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!

JY

Oct 30, 2018

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.

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1851 — 1875 de {totalReviews} Avaliações para o Sequence Models

por Aditya T

Jul 01, 2018

Excellent series of courses! Loved the lectures and thoroughly enjoyed the exercises! A big thanks to Andrew Ng and all the instructors and mentors. The forums provided useful hints on the couple of occasions I was stuck. While I would have initially suggested providing more info on Keras APIs, in hindsite the additional time spent in searching Keras documentation was useful arriving at better understanding of the infrastructure.

por Aditya C

Feb 21, 2018

The Literature for RNN's was not motivating enough compared to Convolutional Networks and the previous courses. However, Andrew did concentrate on the important aspects which would help us in building RNN ourselves. I did feel the assignments were not as elaborative and extensive as the CNN's but I understand the idea behind it (being just to make users aware of the skeleton of the model instead of doing everything from scratch).

por Peter G

Feb 20, 2018

This one is much better then the previous one - Coursera team definitely made their homework. However some theoretical blank-spaces are still left. For instance - nothing is said about how recursive gates are being updated during BPTT backward pass. For someone who has the some experience and read some other sources that is not a big deal, but for a complete first-timer who pays attention and uses his brain - this is a pure flaw.

por Stephen R

May 29, 2018

Very interesting application of deep learning. Gives a good overview. Assignments are fun, yet it's too easy to complete the assignments without understanding the big picture. I found the "attitude" assignment in week 3 a bit difficult to grasp however. Particularly liked the dinosaur names, emojify, the humour and positivity in the course: mentioning gender/racial bias, encouraging people to do good with their skills. Namaste!

por João A J d S

Jul 28, 2019

The only trouble with this course is that we're talking about seriously deep networks. That means it's difficult to present working, practical cases (jupyter notebooks) to work all the steps.

Still, I'd recommend presenting more and simpler steps towards building an RNN (particularly an LSTM). I had to come back to the notebooks several times... and honestly, I think I'll get back there again to try and understand better...

por Dave J

May 03, 2020

Very good overall. Andrew Ng explains the material clearly and accessibly.

I'm deducting a star for occasional issues that get picked up by volunteer moderators on forums, who do a great job, but seem not to get corrected by Coursera staff. Also for one or two small inconsistencies in terminology between lectures and programming exercises. However I've seen much worse and more confusing inconsistencies in other courses.

por Ankush K

May 11, 2020

I thought the course and the specialization was great for people who want to get into the details of deep learning. Although I enjoyed learning about all the details, I wish there was a separate course specifically for Keras and TensorFlow. In practice, we will rarely have to implement the models from scratch, and having a better understanding of Keras and TensorFlow would be more helpful in terms of career prospects.

por Eli C

Apr 29, 2018

Andrew has a very good video-lecture style.

The programming exercises can be a bit frustrating at times for the wrong reasons, but at this point the course has been available long enough that you should be able to find a thread in the Discussion forum that provides enough hints to resolve any issue you might encounter. Nonetheless I appreciate the effort that went into designing the programming assignments.

por Damian S

Feb 20, 2018

Presentation is amazing... Professor Ng always does fantastic job of communicating the material in a clear and easily understood way.

I took the course on the first run-through, and there were still some kinks in the grading process that were a little frustrating to deal with, but hopefully these will be ironed out for later versions.

Thank you, Professor Ng, and everyone else involved. You never disappoint!!

por Michael D

Apr 02, 2018

This course has excellent content. Unfortunately there seems to be a slight drop in quality compared to the other courses in this series, with respect to the programming assignments. I didn't find them to be very clearly explained or illuminating.

I'd recommend the jupyter notebooks be reworked with better explanations and more attention to the notational conventions.

Still an excellent introduction though.

por Jeff B

Mar 02, 2018

The lectures were outstanding (as usual), but the programming assignments (except for the final Trigger Word assignment) were terrible. I spent almost all my time trying to figure out Keras syntax, without ever having a Keras tutorial or anything. If you are going to rely on Keras, you should probably add a tutorial or some references. A lot of wasted time. But other than that, this course was amazing.

por Ishwarya M

Jun 30, 2018

Very good course. I liked the speech recognition part more. I found the assignments involving Keras code difficult to do in both RNN and CNN courses. Without the help of discussion forum i wouldn't have completed the Keras assignments. Thank you all the fellow students and mentors for your contributions to the discussion forum. Thank you so much Andrew and team for putting this awesome specialization :)

por Ivana S

Apr 19, 2018

As the other courses in this series, this is definitely another great course, and explains to details the various sequence models. I gave it 4 stars because I believe it might need some improvements. Compared to the previous courses it felt a little rushed, and had too much new information and long programming exercises for a single week. Maybe it would have been better if it was 4 weeks instead of 3.

por Cristina B

Mar 04, 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)

por 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.

por 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.

por 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.

por 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.

por Daniel Z

Aug 15, 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.

por 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.

por 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.

por Nicolás A

Feb 19, 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.

por 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.

por 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.

por 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.