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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

26,455 ratings
3,120 reviews

About the Course

In the fifth course of the Deep Learning Specialization, you will become familiar with NLP models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and more that have become possible with the evolution of sequence algorithms thanks to deep learning. By the end, you will be able to build and train Recurrent Neural Networks 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. DeepLearning.AI is proud to partner with NVIDIA Deep Learning Institute (DLI) to provide a programming assignment on Machine Translation with Deep Learning. Get an opportunity to build a deep learning project with leading-edge techniques using industry-relevant use cases. The Deep Learning Specialization is our 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 gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

Mar 13, 2018

I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!

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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2476 - 2500 of 3,091 Reviews for Sequence Models

By Jeremy O

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

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

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

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

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

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

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

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

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

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

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

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

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 Abid

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

By Harry L

Jul 16, 2018

Overall it was pretty informational on introducing NLP to me. However, Keras was a little bit frustrating to learn at the beginning. I found out the forum was a very good resource to learn Keras syntax whenever I was stuck.

By Eric C

Jan 12, 2020

Great course! I do feel like I'm just scratching the surface of the types of applications that I can make. I think the coding segments still hold our hands a little too much, but you can't beat the clarity of the lectures.

By Nguyen H S

Oct 21, 2018

The course lecture is grade but I hope the assignment is better in guiding structure, something the explanation is hard to follow, and the assignment should include the transfer learning instead of using the trained model.

By Paolo S

Jun 8, 2019

This was hard to keep up with, maybe too hard. The assignments' difficulty also was on a different level then the lectures maybe there more time should be put into the lecture videos as it was the case for DNN and RNN.

By Aida E

Feb 21, 2018

The videos and programming exercises were very interesting and insightful. My only complain is some of notebooks for exercises include errors and it was just a time-wasting task to find the "trick" to pass the grader.

By Anshuman M

Jul 30, 2018

The content is well captured and Andrew really helps build the required intuitions. But, the assignments are too guided. There is no room to struggle for solutions which often proves to be the main source of learning.

By Prateekraj S

Jul 28, 2020

The exercises are too short and too basic for this course specifically. The task is a great learning experience but there is not much one would struggle with in terms of difficulty as there is too much spoon feeding.

By Ivan

Mar 18, 2019

Great video lectures, but practical assignments are a pain due to awful auto-grading system and programming expirience in Jupyter in general. Most of the time you'll be searching for an error that isn't really there.

By Fabio R

Oct 31, 2020

Excellent course, excellent lecturer. Unfortunately some of the test data (week3/lab/trigger word detection/XY_dev/* CANNOT BE DOWNLOADED ... The programming lab sections are nice - sometime a bit too helped ... ;)

By Jeffrey D

Mar 11, 2020

Programming exercises did show you quite a bit, but got complex enough that most of my time was spent reading and understanding the preamble than doing any programming. That being said it delivered on the promise.

By Salamat B

Sep 24, 2018

Course content is really good! However, I found it quite difficult to truly understand deep learning algorithms. However, it provides good glimpse of of sequence models and intuitions behind various useful models.