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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
29,838 ratings

About the Course

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....

Top reviews

MK

Mar 13, 2024

Cant express how thankful I am to Andrew Ng, literally thought me from start to finish when my school didnt touch about it, learn a lot and decided to use my knowledge and apply to real world projects

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.

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2901 - 2925 of 3,619 Reviews for Sequence Models

By Volker B

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Sep 21, 2023

Great course, it will help much to be more creative using deep learning within own projects. But the last week about transformers could have been more detailed/in-depth - leaving with the feeling having some "gaps" there, especially regarding the math.

By Harish K L

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Oct 15, 2020

Compared to the previous 4 courses in this specialization, I felt this course a bit less on details. It may be just me not having the required level of understanding. It just felt like I could've used a little more details. Andrew is awesome as always.

By Daniel S

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Aug 30, 2020

The programming assignments were pretty hard this time. I think, Andrew should spend more time to explain the concepts in the video lectures. Took me a while to get this stuff since it is a little bit more abstract than the previous specializations..

By Endre S

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Sep 18, 2018

This last course of the series while still being excellent, it had a few minor issues in the assignments and was quite hard compared to the previous four. Nevertheless, I still learned a lot from it and I am really grateful for it being available.

By mail@robertoarnetoli.com

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Aug 6, 2018

Very interesting and well taught course. The only disappointment is that it focuses almost completely on NLP. I would have much preferred working on other topics too, like for example time series with LSTM, which instead didn't even get mentioned.

By Jkernec

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Feb 15, 2018

You should try to leave access to the previous code I wrote in the previous weeks or help out a little in week2 exercise I really struggled to get some of the code done because I didn't have access to my previous notebooks because you locked them

By fabio d s

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May 29, 2022

The course is very interesting and well organized. Certainly the content is appropriate for the cost of the course. The only flaw, for me, is the lack of mathematical demonstrations, but I know it is impossible to cover this in a single course

By Harshad K

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May 4, 2020

This course helps you build the basics for natural language processing using deep learning methods.

The assignments at the end of every week test your understanding of the subject and improves your understanding of the topic. Highly Recommended

By Carlos A L P

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Jan 4, 2021

Good continuation of RNNs covering theory and Python exercises using a few algorithms and uses cases. I would love to see more content and more interesting examples to implement in Python. Still, this is a nice introduction to sequence models

By Semion B

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Aug 29, 2022

Amazing information. My one complaint is that the assignments don't help you understand how to work with TensorFlow. Most of the time I would read the documentation on the functions and follow the syntax. Maybe it simply comes with practice.

By Jeremy O

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Apr 9, 2021

I really liked it, however I don't feel like it really went into some of the more practicle issues with sequence models. I was left feeling like I wouldn't really know what to do in a situation where I had highly variable sequence lengths.

By Paulo M

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Oct 7, 2020

I preferred the first specialization courses. The explanations are not so clear as the explanations in the first courses. I will make the NLP specialization to have a better understanding. Anyway, I recommend the specialization. Very good!

By Óscar G V

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Jan 27, 2019

It is a very good course. Andrew Ng explanations are very clear and easy to understand with a lot of good examples. On the other hand there are some confusions or errors in the backpropagation part of the programming assignment about LSTM.

By Sajal J

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Jul 22, 2020

I am rating this course 4 because It doesn't give any guidance about future career paths and next things to learn. The explanations are very good. I understood complex things like GRU, LSTM, Bidirectional RNN, attention model very well.

By Diego A P B

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Mar 7, 2018

While a great introduction on RNNs, I felt there could be another week of lectures given the complexity of the algorithms being explained. Likewise, the programming exercises felt unpolished in some parts, like in the expected outputs.

By Luiz C

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Feb 11, 2018

Very good. To make it perfect, would have liked it for the Assignments to have less bugs (cf. LSTM backprop), and a longer course with extra weeks to present LSTM in the context of prediction (finance, weather, pattern recognition,...)

By Michael M

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Nov 2, 2018

Great course! only negative is that problems would really hold your hand. I don't think there is any way I would pass a whiteboard test on any of this (then again a course to get me to that level would have to be double this length).

By Joshua H

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Jul 20, 2020

The course covers one of the most influential developments in deep learning in recent times, and does so in a thorough way, introducing majority of the relevant mathematics and methods necessary to build a variety of sequence models.

By Jaiganesh P

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Feb 18, 2019

The course is really good if you want to get a good understanding on the basics of deep learning. It would have been great if the course had more hand's on assignments than fill in the blanks kind of assignments in ipython notebook.

By Rohit K

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Jul 7, 2019

I learnt a lot from this course and the whole specialization. I am grateful to the mentors and instructors. If coursera gives me opportunity I can also be mentor for the specialization to help the newcomers through the assignments.

By Ghassen B

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Oct 17, 2019

During the first week, I think that a deeper explanation of the matrices' dimensions throughout the NNs should be given. Indeed, this would be helpful to understand some concepts.

Apart from that, it was an awesoome course, thanks!

By Stéphane M

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Jun 22, 2018

The course was good except first week. I did not learn as much as I would like from the programming exercises of week 1. It could be nice to have 4 weeks instead of 3 for this course. Taking more time to cover the week 1 material.

By Shrishti K

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Jun 26, 2020

Everything is perfect, the teaching is excellent, the only problem is the jupyter notebook, its sometimes difficult to debug issues and takes a lot of time and is kind of vague as well in terms of application of the lectures.

By M H E

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Feb 20, 2023

Thanks to Andrew Ng and his amazing team, this course challenged my fundamental knowledge of Tensorflow and ML algorithms in the most useful way. However, I expected more applying libraries to design the NLP recent products.

By Abid O

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May 1, 2018

some topics not explained in detail. Not enough examples to understand some models completely. As an example, I didn't fully understand what are the parameters for the models, their shapes, and how they are used in the model