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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
30,692 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

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.

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3451 - 3475 of 3,750 Reviews for Sequence Models

By Enzo D

•

Apr 13, 2018

Very usefull!!!

By Mohamad T

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Jan 27, 2022

too long- good

By guggilla s

•

Mar 9, 2019

easy to learn

By Sayon S

•

Jun 11, 2018

A bit cryptic

By Phoenix A

•

Mar 28, 2018

Good material

By yanhang

•

Jul 17, 2019

very useful

By Paul A

•

Feb 24, 2019

a bit hard!

By Sonia D

•

Jan 30, 2019

Very Useful

By BILLA N R

•

Apr 18, 2020

productive

By Roberto J

•

Feb 14, 2018

Thank you.

By Ariel H

•

Oct 13, 2018

Excellent

By Iraneus O

•

Jan 3, 2025

Helpful!

By OMKAR S

•

Jun 1, 2020

Average

By Kelyn B T

•

Sep 15, 2022

Great

By Rahil V

•

Oct 3, 2022

Great

By Dave L

•

Jul 10, 2020

good

By Kiran B

•

Apr 5, 2020

nice

By VIGNESHKUMAR R

•

Oct 24, 2019

good

By Shashank V M

•

Sep 16, 2019

Good

By Yashwanth M

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Jul 23, 2019

Good

By Rahila T

•

Nov 15, 2018

Good

By savinay s

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Apr 9, 2018

good

By kms g

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Mar 22, 2018

g

o

o

By Aaradhya S

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Apr 25, 2020

..

By Natalia O

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Oct 4, 2019

in comparison to the previous courses from this sequence, this one is even less structured - ptobably this is because even broader knowledge is tried to be shown in only 3 weeks, but i feel like a lot is skipped between videos (which are ok) and the tasks - in many assignment tasks in this course it is not very well explained what is meant to be done - i mean this especially in case of Keras objects. In many cases it is quite unclear how those classes are supposed to be handled in the context of our task. There are some hints but those are mostly links to documentation (btw, some of the links are no longer up to date), but it is often not too well explained which properties those objects have, what one can do etc. so one ends up with trying using those objects in different configuarations, then googling around, looking on the course forum for the right answer but it is very difficult to derive it. There should be more precise instructions regarding handling Keras objects - the examples in the documentation and in blogs are often much simpler than those from assignments so one ends up not knowing what is going on. In summary - there is a big jump and a big gap between the intuitions in videos (which btw are much more fuzzy than those in first cources in the specialization, the intuitions get more and more superfluous as one doesnt go into detail) and what is being done in the assignments. One thing i really liked about hte previous assignments was that when writing the code one could really know very well what is going on. And this is no longer the case in this course...