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
28,139件の評価

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.

MH

2020年4月21日

Very good. I have no complaints. I though instruction was very clear. Assignments were very helpful and challenging enough that I learned something, but not so challenging that I got stuck too often.

フィルター:

シーケンスモデル: 3326 - 3350 / 3,375 レビュー

by David S

2020年12月19日

Excellent lectures, terrible exercise material. E.g. "You're implementing how to train a model! But we've done the actual training for you already! Your exercise is to add numbers A and B! Number A is 4. Number B is 11! Enter A + B in the box below!" Also, someone did a search-and-replace and converted every sentence into an individual bullet point to reduce readability.

by Sergio F

2019年5月16日

Unfortunately, this course is the less valuable in the specialization. Programming assignment very interesting but no introduction to Keras. To pass the assignments, forum support has been vital. I also found lectures not clear even to the point that to catch some concepts you have to google around for more resources. Unfortunately, I could not suggest this course.

by Guruprasad K

2022年3月9日

Compared to the other courses in the specialization, this appears to lack depth and clarity one could expect. LSTMs and GRUs are somewhat out-dated now, given the speed of innovation in the field, and Transformers are here to stay (for now). Unfortunately, Transformers are very poorly covered.

by Peter B

2018年2月20日

Getting the input parameters correct for the Keras assignments is on par with the satisfaction of dropping a ring, contact lens, or an expensive object into the sink, and spending an hour looking for it inside the disassembled pipes, through built up hair debris and molded dirt.

by SARAVANAN N

2018年3月19日

Overall a great course, thanks to Andrew NG for his great explanations. But a very bad support, I faced many issues in submitting the assignment due to technical issues (notebook not saving) but no dedicated resource to help me. I spend lot of time in resolving my self.

by Sergei S

2019年5月18日

Feels again like authors tried to put everything into just a couple of weeks, thus the course turned out to be messy with lots of details hidden. Even though there was a lot to learn, I am still not sure if I understand correctly how to build a simple sequence model.

by Clement A

2020年8月7日

Really good to understand the basics, however, it doesn't use the latest TF2 and the exercises are either trivial because too much pre-worked, or too hard because it doesn't give the information required to succeed.

This course really needs to be updated.

by Mladen M

2020年7月9日

Programming assignment instructions are not well written (clear), and as a result it is easy to get stuck on something of little relevance to deep learning. Also, I would suggest that you make the lecture notes in written format available.

by Chris M

2019年8月21日

The lectures cover the basic design of the models but don't help teaching you how to actually use them. I learned more by reading blogs to get the programming assignments to work then this course.

by Ashley H

2018年9月14日

Lectures/Videos were excellent, the assignments were very poor (loads of errors in the code not corrected over 7 months since the course went live)

by yuvaraj

2020年12月11日

The course videos were very lengthy and difficult to follow. Many topics discussed in course video were not part of programming assignment

by Simeon S

2020年3月18日

Good introduction to the concepts. Really poor quality videos and exercises. Very frustrating when working on the assignments.

by David L

2020年6月28日

Good lectures. Programming assignments are useless fill-in-the-blank exercises, you don't really learn much from them.

by Thomas A

2019年10月10日

The programming assignments really are like pulling teeth. There's not really enough guidance leading up to them.

by Mark

2018年10月24日

The course videos and the programming assignments were lacking. And there was no support in the forums.

by Jeffrey S

2018年6月2日

Spent more time trying to work around a buggy grader than learning the underlying concepts.

by Frank T

2019年10月23日

Too hard to understand compared to the previous coursed in this specification.

by Dipesh K

2022年8月13日

Tough to comprehend. A little bit of written explanation might have helped.

by Hamid A

2020年11月13日

Was very difficult. please add more expiation of mathematical equations.

by Sukeesh

2020年4月18日

Little unsatisfied with the final part of the specialization.

by Clashing P

2021年9月12日

assignments are very hard and needs lots and lots of search

by Arsh K

2019年8月20日

Lack of Keras training made it often hard to do layer code.

by Tom T

2020年1月9日

This course needs more instruction on Keras.

by Mark N

2018年2月12日

Poor explanation for alot of things

by Milica M

2020年5月10日

boring and uninformative