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学生对 deeplearning.ai 提供的 序列模型 的评价和反馈

4.8
27,878 个评分
3,331 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.

JY

Oct 29, 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:

26 - 序列模型 的 50 个评论(共 3,334 个)

创建者 Rohan G

Jul 18, 2020

Assignments were extremely didactic; there was no room for creativity. They were not transparent and gave a minimal idea of how to implement these things properly. Course moderators did not bother to answer any of my queries, making the course even less intellectually stimulating. The lectures were monotonous, and hence, I was having trouble finding them to be very engaging. Although, the professor did give some insightful points.

In conclusion, I wouldn't recommend this course to someone unless they are extremely novice programmers. Yet, one may refer to the videos to gain some conceptual clarity on specific topics.

创建者 Isaraparb L

Jul 26, 2018

Unfortunately considerably a subpar course compared to the other four in the specialization. Programming assignment is a mess - wrong formulas presented, nowhere near enough Keras's tutorials, etc. Every assignment is passed by browsing the forum looking for help from other people. It is unclear to the point of being annoyed (got someone in the forum cancel his subscription). However, lectures are fine and sequence models cover a wide range of areas/applications, so you can't miss it anyway.

创建者 Kiran M

Feb 16, 2018

This course felt rushed. Especially, the programming assignments, which had many errors and were frustrating at time. It is still worth it since the content is really good -- only if you are willing to go through the frustration during the programming exercises.

创建者 Martin C S

Jul 13, 2019

Assignments don't match the quality of the other four courses of this specialization. Automatic grading accepts solutions despite results not matching expected results. This should be fixed.

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 Jaime G

Jun 27, 2019

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

创建者 AlainH

Feb 5, 2018

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

创建者 Oscarzhao

Apr 2, 2018

some optional exercises are wrong, wasted a lot of time on LSTM backward propagation

创建者 asieh h

Jun 13, 2018

It was difficult to follow the programming exercises because many of it had already been written. I think it would be more useful to learn one framework instead of using both keras and tensorflow in one course. I still don't know how to debug any of these frameworks. Without the forums, it would be very difficult to pass the assignments. Sometimes there were bugs in the jupyter notebook, sometimes typos that were misleading. As a result, it took me many hours stuck on one assignment. It would be good if these comments are taken into account for the future classes of this course. I really enjoy Andrew Ng.'s courses but I was disappointed at this last course's assignments.

创建者 Moses O

Jul 21, 2021

The unit tests in the programming assignments are poorly implemented. They will fail you if your code is not exactly as expected, even when it runs and returns the correct output.

创建者 Saksham G

Apr 19, 2020

TensorFlow and Keras basics are not covered. The course states no pre-requisites as well. This was really disappointing.

创建者 Yanzeng L

Feb 17, 2019

There are a lot of mistakes in programming assignment. Please update and fix it

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 Ozioma N

Jun 9, 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 Marcin G

Feb 1, 2018

Amazing course. Andrew Ng has exceptional talent to explain complicated concepts. I have heard about RNNs in other courses but this is the first course, that actually made me understand them. Highly recommended.

创建者 Ahmad B E

Feb 4, 2018

Best simple course for Deep Learning. I think this specialization is the best as a MOOC but it can be better as an academic course.

创建者 Jizhou Y

Mar 1, 2019

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

创建者 Oleh S

Jun 3, 2020

Very good course which gives a nice intuition to sequence deep learning modelling. Unfortunately, this is the weakest one among the whole specialization. There are no deep explanation of LSTM as well as GRU and back-propagation algorithm. Seq2seq models explanation is not clear and looks too inconsistent. I had to read a lot of the additional materials and blogs in order to understood a theory behind lectures. Hence, the first week assignments were disagreeably difficult to complete, whereas second and third week assignments were comparatively easy. I think this course should be revised or prolonged for 4 weeks to cover LSTM models more profoundly. Nevertheless, I would like to thank Prof. Andrew Ng for really great job and initiatives in such an important area of study!

创建者 Beibit

Jun 25, 2019

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

创建者 Ravi K S

May 19, 2019

Could have been more thorough like previous courses

创建者 Adrian S

May 21, 2021

I would really like to give this course 5 * but the finally programming assignment was a disappointment. It seems many other folks feel the same way. I found myself spending many hours trawling the the web for additional background.

创建者 Navid A

Aug 27, 2020

The first week is amazing. The last week is the worst! Andrew starts nicely; but as he goes to the second and third weeks, he hardly explains why he does what he does.

创建者 Zelidrag H

Jul 26, 2021

Week 4 coding exercise is incomparably harder than any other in this entire specialization.

创建者 Siddharth S

May 29, 2021

The transformer subclass programming exercise is super useless task. Spent hours on this task and learnt nothing.

创建者 chao z

Feb 22, 2018

If it could improve assignment accuracy, it will be better