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

4.8
18,174 个评分
1,970 个审阅

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

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.

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.

筛选依据:

1 - Sequence Models 的 25 个评论(共 1,949 个)

创建者 Dylan R

Oct 20, 2018

Tons of editing errors in lectures, and the programming problems rely more on knowledge of Keras (essentially untaught throughout the course) than they do on understanding of lecture material. A disgraceful end to an otherwise solid course sequence.

创建者 Bogdan P

Nov 03, 2018

I really like the deeplearning.ai specialization. And also I like the Sequence Models course. However, I feel that I have learned less during this course comparing to the other ones in the specialization. First, I believe it was an extensive use of Keras. Whereas the framework is great, it would be much better for understanding if all the exercises were in numpy, whereas Keras tween-projects be optional. Doing both numpy and Keras versions would allow to better understand the material and learn through repetition. Second, even though the course is great, I perceived the number of errors/typos was much higher than in other courses. Is that true? For example, the Jazz Improvisation exercise was a nightmare. Overall, thank you for the course. Despite those problems, I would still recommend it.

创建者 Lewis C L

Apr 15, 2019

Full of appalling errors that have been present for over 1 year. No one fixes it. It is clear that since Ng was let go by Stanford and Baidu, he is trying to earn a living with deep learning_ai. This apparently is not working as the small income from Coursera is not sufficient. As a result the prerecorded classes remain on Coursera to accrue some residual income. But, Andrew Ng and the staff are apparently gone.

Sadly, since these classes are no longer based on REAL Stanford classes the quality has gone downhill. I would recommend not taking the deeplearng_ai classes. Stick to classes offered by currently employed professors at established universities--preferably classes that ARE the same as the university classes or, at least, those derived from actual classes.

创建者 Sonia B

Feb 19, 2018

Loved the course - it was very interesting. It is also pretty complex, so will probably go through it again to review the concepts and how the models work. Thank you for this wonderful course series!

创建者 Ka W P N

Apr 11, 2019

If not Internet, I would not have been able to study a world-class Deep Learning course at an affordable price. Thanks Andrew and team.

创建者 Alex R

Jun 15, 2018

Keras is required to pass the assignments but no training provided for it. I can learn it myself of course but then the question is this - what am I paying for?

创建者 Jialin Y

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.

创建者 Jinxiang R

May 26, 2019

I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise

创建者 Andrés F R

Nov 08, 2018

I want to thank Andrew Ng and his team for the amazing work. You definitely make the world a better place sharing this knowledge, and it is an inspiration.

To the contents: the course covers many uses of sequence models, for many different formats (many-to-one, many-to-many...), the questionnaires are focused but comprehensive and the programming exercises cover a wide range of difficulty levels, from no-brainer-one-liners (most of them) to implementing LSTM backprop by hand (optional). They take away the dirty work from you but make sure you get how you would do it. At the end you get to work with pretty complex setups like the attention model, but you still get the feeling of knowing how it ticks from the very bottom up.

The actual merit is that, even if it feels simple, it actually does work and is a takeaway knowledge that can be directly applied for personal setups. And mr Ng's videos are a charm, you can totally feel the care. Glad to see him back after so many years :)

Cheers

创建者 Benjamin F K

Dec 06, 2018

5 stars for the very informative lectures. I especially appreciated the amount of content related to de-biasing models. Such an important step to take!

2 stars for the HW assignments, however. I felt like I was just translating comments into code and that I didn't really learn enough to do sequence model development on my own without the hand-holding.

创建者 redfoxbluefox

Apr 05, 2018

This course is by far the weakest out of the 5 course sequence. I did well in it (96.8%) but I think the programming exercises did not help build understanding of sequence models. Often I found myself just trying to get through the programming because I felt it was more an exercise in reading Keras documentation. I think you can pass this course without a solid understanding of what is going on in the sequence models. The programming exercises should be revamped to focus more on understanding what is happening in the program rather than trying to figure out Keras syntax (which is also useful, but perhaps better suited for a prep course).

创建者 Nathan P

Feb 20, 2019

I'm blown away by how quickly this series of courses brought me from thinking a neural network was a magic box full of fairy dust, to being able to understand even the (al)most complex of network architectures and what makes them tick at every level at a glance. A lot of time has obviously gone into structuring this course; not an ounce of fat present and the format of developing intuition before diving into the nitty gritty and optional further learning resonates with me on so many levels. Thank you Andrew Ng and the team at deepmind.ai and coursera!

创建者 khushal m

Apr 11, 2019

I think it is the best courses designed so far. Gives you exactly the appropriate amount of information needed to understand basics behind sequence models. A must do course for all the students who want to pursue a career in this field.

创建者 Jizhou Y

Mar 02, 2019

Professor Andrew is really knowledgeable. I learn a lot from his lecture videos.

创建者 Stanley C D

Sep 28, 2018

Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.

创建者 Abhijeet M

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.

创建者 Ozioma N

Jun 09, 2019

Great module, I am lucky to have used this resources in learning sequence models, I can imagine running LSTM using one of the frameworks without ever implementing it myself, Andrew Ng/Deeplearning.ai is the best!

创建者 Ning M

Feb 21, 2018

Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.

创建者 Jason J D

Sep 11, 2019

Wonderful end to this Deep Learning Specialization. The programming assignments cover up a variety of hot topics in the Deep Learning market. The videos are very well made and teach the content in depth. A special thanks to Prof. Andrew for yet another amazing course in this wonderful specialization!

创建者 Ravi K S

May 20, 2019

Could have been more thorough like previous courses

创建者 Johannes J

Jun 27, 2019

Great insights, helpful notebooks, good explanations.

创建者 Beibit

Jun 26, 2019

Little bit math heavy. It was sometimes hard to understand the intuition, e.g. RNN, LSTM, GRU

创建者 Jaime G

Jun 27, 2019

Some coding assignments were too hard to follow what was required.

创建者 Marc B

Jul 12, 2018

This one went a little fast for me, can't say that I'm confident on the shapes of tensors going through RNNs and why

创建者 AlainH

Feb 05, 2018

This course has many inconsistencies and errors in the homework. Seems like a rushed job.