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学生对 deeplearning.ai 提供的 Sequence Models 的评价和反馈

4.8
21,687 个评分
2,476 条评论

课程概述

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....

热门审阅

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!

AM

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.

筛选依据:

1851 - Sequence Models 的 1875 个评论(共 2,450 个)

创建者 Brad M

Aug 22, 2019

A very helpful and enlightening course, though it felt a bit "hand-wavy" at times. It never really felt like we were getting the full story, like I was missing something the whole time. Word embeddings cleared up a lot, but the entire course was a lot of information to digest at once. Coming from an image processing background, most of the terminology was unfamiliar, and the programming assignments weren't quite as guided as previous ones.

In the end, I think it was a great course, and I'd recommend it highly to anyone interested in the field. If you can't apply it to your work, it probably isn't as beneficial.

创建者 Marc A

Mar 26, 2019

I'm a fan of Andrew Ng's machine learning classes on Coursera. This was my least favorite. I'm not sure if it's because of the complexity of the material or that so much material is presented in a short time, but I feel that I'm not as confident about my knowledge of the material in this course compared to the earlier courses. In the last few assignments, I felt like I was mechanically plugging stuff in without really understanding the thought process. His teaching style seems much the same as the other courses though, so it's possible this could be due to me rather than the course.

创建者 Tim S

Jul 11, 2018

I am grateful for the opportunity to have learned from an exceptional instructor, and one of the luminaries, in artificial intelligence. Insofar as this particular course is concerned, theory was well explained, as always. I feel like there was a bit of a disconnect in the implementations, though. Some of this was just the sheer challenge of using a still-unfamiliar platform (Keras). And, in concert with this latter point, some was due to a sort of "fill in the blank" approach to using the platform. Nonetheless, that I have learned, and learned a lot, is undeniable!

创建者 Rhondene J W

Apr 21, 2018

Firstly, thank to the course instructors and Dr.Ng for teaching us deep learning. You are all a gem. I enjoyed this course, and how simple it made coding RNNs. However, I believe the concepts could be simplified some more, even in the form of a pseudocode or conceptual outline. This is my 3rd course from Andrew Ng, so I know he's skilled at distilling deep learning concepts with ease. Week 1 was the best for me as the operation of the LSTM, GRU RNNs were succinctly outlined and set a solid foundation , Week 2 could be presented a less abstract way though.

创建者 Nkululeko N

May 02, 2020

I think with sequence models, the course details were very challenging. I strongly believe that do take a course in Deeplearning Specialization, one must at least learn Python from basics to advanced level. However, Andrew Ng has made it easy for a first time student with programming background to understand most of the concepts in this specialization. Thank you Deeplearning.ai for this course. I have learned some of the cutting-edge skills that can't be easily found anywhere. I have learned a skill that will set me apart from the crowd.

创建者 John B

Sep 20, 2018

Great content, and leaves me set to build systems making predictions for or conversions between sequences- particularly including text posts, which are an interest of mine.

Deducted a star because a couple of ungraded exercises contained errors which had been left uncorrected; they were still valuable, especially the manual implementation of backprop one, but there's some missing attention to detail there. But the level and effectiveness and practical applicability of the course remains excellent and I'd still heavily recommend it.

创建者 Shivdas P

Jan 05, 2020

I found the first week of this course a bit tough compared to all the other 4 courses in this specialization. Perhaps there should be one more week to give much more programming exerises to help understand the concepts clearly. But having said that, the last two weeks, especially the last one about hot-word, is very neatly done and provides very good understanding of such models are implemented. Overall satisfied. Thanks Andrew and team, I feel much more confident in my understanding of these terms and the concepts behind them.

创建者 MC W

Apr 10, 2018

I never been exposed to this subject Sequence Models before. I learned a lot from this course. But the materials is more advanced than all previous ones, especially the program exercises. The exercise guideline is helpful but not leave many guess works for students not well skilled in Python and Keras. I completed the program exercises by blindly trying different keras commands.

Little suggestion: include a short but complete example code for building Keras Sequence models in the tutorial.

Over all, a great course. Thanks a lot.

创建者 Kai H

Feb 10, 2019

Overall, it is very good course unless for some minor problems with the assignments.

For example, in Week1 the optional assignment, there are many bugs there, one may waste a lot of time trying to figure out the correct solutions. Though, it has been widely discussed in the forum, the instructors should have updated the material or at least warn the students somewhere in the assignment to read forum ahead of time. You must admit that many won't resort to the forum only after trying and wasting enough time..

Hope may help.

创建者 Conor G

Nov 06, 2018

Much more challenging than the other courses in the DL specialisation. It forced me to delve a little deeper into the topic in order to overcome obstacles in the assignments. Content-wise, it's a great introduction to DL for NLP. Professor Ng's explanations are perfect.

Admittedly, compared to the other courses, this one is "messier". Spelling mistakes, some contradictory instructions, and a somewhat broken notebook for the last assignment. It felt rushed and I'm surprised that a lot of the errors haven't been fixed yet.

创建者 Zhu L

Feb 23, 2018

The course itself is cutting-edge, so a 5-star for this.

But the following amount to a -1 star:

1 Too sloppy, lots of typos.

2 Wrong answers wrong expected values in the notebook.

3 Grading server sometimes runs slow.

4 Saving the notebook fails quite often.

5 Too much is done for the learners, while you could've make the programming assignments more challenging.

6 Deep learning itself has too much black magic and inexplanability in it.

I'm quite sure that harsher comments and a few 2-star or 3-star will be among the reviews.

创建者 Ed S

Oct 21, 2018

It's a good intro to RNNs (LSTMS and GRU). Very interesting use cases for RNNs. I feel that there could have been more room to try more programming exercises for different use cases & RNN architectures. Be aware that Keras is very sensitive to changes and you will find yourself reloading the jupyter kernels repeateadly when you get stuck. This is not a problem of the course itself but it is something that could end up wasting a lot of your time chasing problems when your code actually should work.

创建者 Joseph C

Apr 19, 2018

Another great course by Andrew Ng! This course is part of the CS230 class currently being taught at Stanford University. Only reason for 4 rather than 5 stars is that at this stage (April 2018), there are few knowledgeable mentors and virtually no Instructors present in the Forum. Course provides little introduction to the syntax of Keras, which makes for some problems implementing models. Therefore one might spend a lot of time spinning one's wheels until finding a way forward.

创建者 Andreas B O

Jan 23, 2020

As with all the 5 courses in the Deep Learning Specialization, the video lectures were amazing, thoughtfully designed (and separated) and gave an understandable overview of the content. As for the programming assignements, some lack a clear description of what is to do - that mostly concerns single steps withing a sub-task though. Tensorflow and Keras need a considerable amount of self-study next to the lectures to truly understand what you are doing there.

创建者 Guilherme Z

Sep 04, 2019

I enjoyed this course very much. The videos were very informative covering a lot of ground in RNNs. I also enjoyed the assignments which covered both implementation of RNNs from scratch to get a good feel for it, and practical implementations. I was a bit disappointed about NLP section as it brushed over word embedding and left me without much understanding on how they are estimated. I would also like to have seen time-series covered in this course.

创建者 Michał K

Jun 11, 2019

I loved all of the courses in the specialization. However, last two (sequence models and convolutional NNs) had in my opinion poor exercises, not well described, or emphasizing the parts which are not that important, omitting at the same time more important topics. For example the last exercise with spectogram was mainly focused on preparing the data rather than explaining/practicing algorithms. All in all, I gave 4/5 which is still very good grade.

创建者 Serkan Ö

Jun 25, 2018

I dont understand why notebooks are become unavailable when I am working on it. It says method not allowed and then please login through www.coursera.org. Then I had to run all the cells again. I think this is because of the lack of resources like # of servers available. Other than that, like the content of the programming assignments. Especially the trigger word detection algorithm worked perfect with my own voice, that was satisfying of course.

创建者 Betiana F

May 31, 2020

This course is a great introduction to sequence models and a great way of finishing the specialization. All main areas were covered. It is a good entry point for those who want to keep improving their sequence-models skills.

Keep in mind that Keras is needed (not a basic level). In comparison with the other courses, the exercises here sometimes are more focused on the preprocessing that in the model itself. Nevertheless, more than recommended.

创建者 Erik B

May 04, 2018

I got an overview of how people use this technology but the whole network architecture and especially dimensioning remains to be somewhat of a black art.

The overview is much better then one could obtain by downloading tools, or reading framework-centric books. It provides also a lot of information through the references to the scientific literature.

It is clear that this is a field still in its infancy but the results are already amazing.

创建者 Sandeep J

Feb 22, 2018

Awesome course. It feels like this one was more rushed than the others in the Specialization. I am a bit concerned whether the "Specialization" has become a "Survey" of the course, and leans heavily on the assignments for teaching..but then, could do more for why some architectures are the way they are. The assignments improved from being a spoon-feeding exercise. That's good. But, on the other side, Keras documentation was confusing.

创建者 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.

创建者 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).

创建者 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.

创建者 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!

创建者 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...