Chevron Left
シーケンスモデル に戻る

deeplearning.ai による シーケンスモデル の受講者のレビューおよびフィードバック

4.8
27,835件の評価
3,328件のレビュー

コースについて

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....

人気のレビュー

AM

2019年6月30日

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

2018年10月29日

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.

フィルター:

シーケンスモデル: 26 - 50 / 3,329 レビュー

by Rohan G

2020年7月18日

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.

by Isaraparb L

2018年7月26日

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.

by Kiran M

2018年2月16日

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.

by Martin C S

2019年7月13日

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.

by Marc B

2018年7月12日

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

by Jaime G

2019年6月27日

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

by AlainH

2018年2月5日

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

by Oscarzhao

2018年4月2日

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

by asieh h

2018年6月13日

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.

by Moses O

2021年7月21日

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.

by Saksham G

2020年4月19日

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

by Yanzeng L

2019年2月17日

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

by Jason J D

2019年9月11日

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!

by Ozioma N

2019年6月9日

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!

by Marcin G

2018年2月1日

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.

by Ahmad B E

2018年2月4日

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.

by Jizhou Y

2019年3月1日

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

by Oleh S

2020年6月3日

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!

by Beibit

2019年6月25日

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

by Ravi K S

2019年5月19日

Could have been more thorough like previous courses

by Adrian S

2021年5月21日

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.

by Navid A

2020年8月27日

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.

by Zelidrag H

2021年7月26日

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

by Siddharth S

2021年5月29日

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

by chao z

2018年2月22日

If it could improve assignment accuracy, it will be better