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

4.8
27,127 个评分
3,230 条评论

课程概述

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....

热门审阅

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.

SD
Sep 27, 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.

筛选依据:

2551 - Sequence Models 的 2575 个评论(共 3,225 个)

创建者 Pedro H B D

Aug 30, 2019

There was not enough theory as the first three courses. Some explanations were superficial and difficult to understand. Maybe drawing the shape of the inputs, outputs, and other matrices in lectures would help to better visualize what's going on inside the networks. Overall, it was a great course.

创建者 Miguel S

Nov 27, 2019

It's a great course. The information and knowledge that you get about Sequence Models is fantastic as a primer. Andrew is an amazing teacher throughout the entire specialization altough I found the content of the videos in the Sequence Models slightly more rushed than the previous 4 courses...

创建者 Daan v d M

Nov 16, 2020

The theory was absolutely interesting and an eye-opener. The programming exercises were hard to make because of the keras/tensorflow knowledge and I actually ended up just fill-in in things, as were given in the examples, without really knowing what I was doing. Time for a Tensorflow course.

创建者 Thiago H M

Apr 18, 2019

Poderia dar um pouco mais de instruções na hora de usar as funções do Keras. Ficou um pouco confuso.

No assignment da semana 3 (Machine translation). Tem um output que não precisa estar exatamente igual mas o curso não fala isso e acabei gastando bastante tempo nisso, só vi depois no fórum.

创建者 Sujay B

Apr 9, 2018

Though the lessons are interesting and mathematically demanding, I felt that it was a good time spent learning these concepts. Overall I feel that the 3 weeks could be split into 4 weeks and learning could have been much smoother by adding some more lessons to address the contents of Week 2.

创建者 Anna C

Jun 23, 2019

Nice content, but the assignments are too easy and only demonstrate the pipeline instead of providing hand-on experience in picking the network and training with GPU. Also, there are some grader problems which has wasted my time to make my code pass the grader even if the answer is correct.

创建者 Jingbo L

Mar 16, 2018

The homework grading methods need improvement. I got the right model and get the right results, but still have to spend tons of time to make the submission pass the grading system. It is a waste of time for future learning. You may want to train a DL model to solve this grading problem :).

创建者 Richard M

Oct 25, 2020

Great explanation of theory (RNNs etc.) and easy to follow course structure. The programming exercises are disappointing though: They mostly consist of mindlessly copying Keras functions without an understandable (!) explanation. Many provided links to the Keras documentation are outdated.

创建者 Ali B

Sep 1, 2019

Obviously, The professor and TAs have put a lot of time for preparation of this course, and I really appreciate it. However, the hws of the course is too much focused on language translation. They could put another examples, say business data, to represent other applications of RNN/LSTMs.

创建者 Chenyue W

Feb 8, 2019

The course should provide more instruction on the Keras and Tensorflow, since the notebook is largely dependent on the knowledge of these frameworks. Moreover, the logic of programming is not so well-organized: I personally prefer to have my own logic instead of modules got implemented :)

创建者 Lukas K

Sep 3, 2018

Really interesting course with overview about sequence models and what can you do with them. Lectures from prof. NG are amazing as usual. The only thing I was missing was maybe more tutorials on Keras LSTM usage. The exercises on LSTM were quite confusing, especially using shared layers.

创建者 Seyyed M A D

Mar 6, 2020

Very Important !!!

Hi,

We do need more programming assignments in order to master the material. We joined Andrew's courses to master (not just get introduced to) the materials, because Andrew and the rest of the team is awesome.

Thank you very very much for all your time and consideration

创建者 Faraz H

Mar 13, 2019

I am overwhelmed by too much material. Additionally Tensorflow and Keras syntax is not very elegant or coherent as they are such high-level languages. I learnt a lot at a high-level overview in this course, but my fundamental understanding was consolidated in the previous 4 courses.

创建者 BlueBird

Sep 7, 2019

Finally, the last course was completed. For me, this course is very difficult, because the content of the course is somewhat obscure and difficult to understand. But I learned some basic knowledge about Natural Language Processing and Speech Recognition through this course. Thanks!

创建者 Carolina F

Jan 8, 2020

This is my third course in Deep Learning, the contents and pace of learning are great, they provide a good level of understanding in the subject. The notebooks have bugs and I wasted a lot of times making them work, thus they could be improved to use that time actually learning.

创建者 Osman F K

Dec 24, 2019

The concepts presented in this course were advanced enough. Yet, the assignments did not require much effort and thinking, which in my opinion is hurting the learning process. If students do not struggle enough with the course, they tend to forget the material they have learned.

创建者 Othman B

Feb 22, 2018

Very interesting courses. I take this as a basis for future applications. I only regret that the exercises are too guided. I can't pretend to be able to accomplish a project in machine learning :-(

I would recommend also to note all the references to the papers, they are helpful.

创建者 Cosmin D

Sep 26, 2018

Great content, assignments are fun and reasonably instructive (although they contain the occasional error and the video editing for the lecture content seems a bit rushed at times). I would recommend this course as an introduction to recurrent neural networks and related ideas.

创建者 Ernest W

Jul 8, 2021

Course is about recurrent neural networks, natural language processing and basics of speech recognition. Valuable content and great delivery by the author expect the final week where it's difficult to understand the transformers network and the related programming assignment.

创建者 Justin P

Apr 19, 2020

Very informative and well taught course on sequence models. The amount of content and pacing was just right as not to be overwhelmingly complicated. There are a few bugs here and there in the programming exercise which can lead to a lot of headaches but overall a good course!

创建者 Sergei S

Aug 19, 2020

Great course, with interesting programming assignments, but still, I couldn't catch intuition about GRU and LSTM nature (I understood its pupuse and equations but couldn't get why exactly THAT combination of equations is necessary to allow RNN learn long term dependencies).

创建者 Mikhail M

Jun 11, 2018

Week 3: quite a complected network was used for trigger word detection; however, it is not clear why exactly this architecture was used; specific order of dropout, batchnorm and GRU seems to be a pure magic; at least, a few words why this combination is picked are needed.

创建者 Elena J

Sep 28, 2020

very good hands-on course. Yet I wished in the programming assignments, it was stated clearer, whether the implemented code is for understanding purposes only (and hence being the reason to be implemented) or is still mandatory even when working within a library (keras).

创建者 Stephen S

Feb 17, 2018

Course content is excellent, I would have given 5 stars, if the Programming assignments wouldn't have bugs. Fortunately people in forum help out with solving issues with assignments. I believe it's due to the short time frame the course is online and bugs get corrected.

创建者 Viliam R

Mar 24, 2018

While this was the most relevant course for me, I missed how it was focused on "helper functions" instead of core RNN concepts. While I feel like I understand concepts like the Bleu score, I would definitely need to spend more time to fully grasp the RNN architecture.