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學生對 提供的 Sequence Models 的評價和反饋

26,455 個評分
3,120 條評論


In the fifth course of the Deep Learning Specialization, you will become familiar with NLP models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and more that have become possible with the evolution of sequence algorithms thanks to deep learning. By the end, you will be able to build and train Recurrent Neural Networks 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. DeepLearning.AI is proud to partner with NVIDIA Deep Learning Institute (DLI) to provide a programming assignment on Machine Translation with Deep Learning. Get an opportunity to build a deep learning project with leading-edge techniques using industry-relevant use cases. The Deep Learning Specialization is our 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 gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....



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!


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.


2876 - Sequence Models 的 2900 個評論(共 3,091 個)

創建者 Gautam D


To be completely honest, I loved Dr. Andrew's method of teaching. But the assignments just flew over my head because I didn't have enough hours of practice of Keras under my belt. I know Keras is there to make things easy but it's very difficult to just trying to pass the grader. To goal of assignments was fantastic, I mean, generating music, etc. sounds really amazing but I feel that if there was some more time given to make us better in Keras and other technicalities then I would've loved this course much more!

創建者 Javedali S


Good but i expected more. The main thing i like about first 3 courses, they were really deep. In the last two courses we have skipped the backpropogation. Now this is something which you can keep optional. I like the way Andrew Ng teaches, going to the basics, and that is why I came here and paid 40 euros per month. Also, there are few stuff missing like Generative models, Adversarial networks, GAN and etc. It would be good if Andrew can have more courses related to this and deep (as it is deep learning :))

創建者 Kush S


By far the most difficult of the 5 courses but giving it a lower review since the programming assignments are rushed through to finish 2-3 in 1 week which gets hectic & understanding of key concepts is lost. Also, it would help if more time is spent in the videos to explain the concept/model/algorithm used in the assignments since I close to understood nothing from the assignments in spite of completing them. Finally, the instructions too were not clear in the assignments.

創建者 John S


Interesting and full of excellent lectures as always for Andrew Ng. The programming assignments quality was not as good as the other courses in the Deep Learning specialisation though. They drop straight into Keras with no information/introduction, use several complex model architectures without explanation, in week 3 4 out of the 5 'your code' exercises were about audio sampling, not very relevant. Again, excellent lectures, just not great programming examples.

創建者 Wolfgang G


Sorry to say they dropped the ball on this one. The last course of this specialisation has the most advanced topics thrown at you in just three weeks, and it's even more cookbook-like than in the previous courses. The material of this part of the specialisation would require a whole course in itself, perhaps for +10 weeks. Here, I found it is at best a guide for self-study, _if_ you have the time for that. Also, support in the forums was very minimal.

創建者 mike b


There are some challenges with the videos eg. repetition, blank audio, variability in speaker's volume (difficult to hear). In particular perhaps 'Bleu score' needs to be redone. I did not enjoy the labs mostly because I don't have much interest in NLP BUT the 'emoji' and 'trigger word' labs were excellent! Especially the 'trigger word' lab should be the standard for all labs, it was very well written: clear, good flow, no mistakes.

創建者 Bradly M


The scope of this course was highly relevant to me, but unfortunately many of the class materials were broken or otherwise incorrect, making some ungraded portions of the assignments difficult or impossible to achieve. Activity on the discussion boards indicates many people have tripped over this for at least the better part of a year, but no corrections have been made. This was quite frustrating and wasted a good amount of my time.

創建者 Yevgen S


I took this course after a long pause after I finished the first 3 courses. I would NOT recommend doing it that way. As a result, I felt rusty on some of the coding practices.

I think the course gives great introductory information on RNNs and LSTMs. The first two weeks of the course are spot on. However, I think the third week is lacking. I had hard time making a connection between the lecture material and the assignments.

創建者 Adam J


This course was at a really high-level and barely scratches the surface of Sequence Models. Didn't really go into much detail behind any of the theory, and the programming assignments were mostly done for us, so you don't really end up learning much. You certainly won't be ready to have a job solving NLP problems after taking this course. If you want that, you're better off going through actual college courses online.

創建者 Md. B U A


First of all, the programming assignments are really copy-pastes. There is nothing really to storm your brain for. Second, many of the ideas presented in the video lectures are very brief and short, skipping the explanation parts. After taking this course, I now know the names of lots of algorithms and models, but that's all I know, only the names. To get broader knowledge on them, I have to look somewhere else now.

創建者 Eero L


The course content and Andrew Ng are great. The submission process of the assignments is absolutely dreadful. You might get 0 points for correct answers or not, depeding on...well, I have no idea on what. Maybe it's Jupyter Notebook, maybe it's Keras or maybe it's something else. But you must have good search engine skills, since you will most likely spend a lot of time in searching the discussion forum for answers.

創建者 Jean


too much information for such a short course. We only get a very superficial understanding of concepts with very little practice to solidify our understanding. The assignments involve implementing very small parts of much bigger systems. I guess the course is ok to get a general idea of the concepts but for deeper understanding of the topics a longer course or multiple courses would be needed.

創建者 Aliaksandr P


This is a very interesting topic. However, I believe the course itself can be improved. I believe there can be more information about NLP and sequence models in lectures. It would be nice to add lectures with practical suggestions about training and tuning sequence models. There were lots of typos and mistakes in notebooks that were found by other fellow students and not addressed by mentors.

創建者 Heyang W


The course overall isn't as good as the previous 4 ones especially for the PA part, I can pass the grader even with wrong output. The PA improvement sometimes just create more discrepancy. The PA is just a walk through of how to building those basis models, but those little bugs will drain extra hours to figure out. I think this course is kind of a prototype one especially on PA part.

創建者 Peter F


Compared to the previous courses, this was a disappointment. There is not as much content as I expected and the homework exercises are not well prepared. If one spends more time with debugging than with "learning concepts" in a basic course like this, then something seems wrong.

Moreover, in a situation where so many people pay so much money (because of Andrew Ng's credit)...

創建者 Vivek G


That was tough, how the weights are stored and their dimensions inside the 'time steps' can be explained by adding one more video, btw the course is awesome if you want to learn the basics of sequence models, you should have completed the previous 4 courses before diving into this. I will always remain thankful to Andrew Ng for providing this type of platform.

創建者 Odinn W


Positives : Excellent lecture material. Assignments broadly are well structured. HIgh bar set by Andrew Ng. Negatives: Assignments have too many errors and mistakes as of Jan 2019 (especially but not only in the optional / ungraded sections) for me to be confortable 100% recommending the course. Instructions for assignments are also not fully fleshed out.

創建者 Sumandeep B


This course is good for introduction to sequence networks, but I felt this is not at par with the previous course 4 (CNN). This feels a bit hurriedly done, with many important things only just touched upon. This should have been a 4 week course like the previous module. Then due attention could have been given to the field of speech, audio, sequence domain.

創建者 Krzysztof J


The course is generally good. However there are some issues with lecture videos editing (some sentences are said multiple times), and with activities (e.g. default settings hardcoded in one of notebooks, didn't let have output shown as reference, also in some cases automated grader has some assumptions, which need to be found using trial and error method).

創建者 Cristian M V V


Great course, great activities and really good programming excercises.

I give it 3 stars because instructors let political views tainted week 3 videos and assignments of this course by introducing some techniques for 'debiasing' and making your neural networks more bias to gender equality political views. That has nothing to do with science.

創建者 Jérôme B


I've got mixed feelings about the whole Specialization. Many very interesting topics, but on the other hands I don't feel like there's any takeaway knowledge for me. Until the very end I've been feeling completely lost in the exercices. I'm proud to have been able to hold on until the end but I'm not sure it's been an useful use of my time.

創建者 Aditya B


Really interesting course with fascinating applications. However, in terms of difficulty, it is a significant step up from all the previous courses. A lot of time is spent figuring out the syntax even though the concepts are crystal clear. ( Probably as it is a collaboration with NVIDIA). The programming assignments could be improved.

創建者 Miguel O


It´s a fairly good course, with lots of cool topics covered on it. My main complain would be that the subjects covered are dense enough to be arranged on a four or even five week course. Instead, for some reason, all the stuff has been squeezed within three weeks, which makes the lectures shallow and rather cryptic most of the time.

創建者 Romain L


The course was great, as ever. But some of the programming exercises were very frustrating. Oscillating from very easy to very difficult, with some unclear (and sometimes erroneous) instructions. I felt this was in sharp contrast with the previous 4 courses of this specialisation, for which the course and exercises were perfect.

創建者 radheem


the course covered a lot of essentials and gave me a rough idea of how stuff NLP and sequence models work. Though at the same time the content often left me confused and overwhelmed. the Convolutional Networks course was far better.

Overall its great work and I am thankful for hard work put behind the complete specialization.