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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
30,390 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

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.

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

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3251 - 3275 of 3,693 Reviews for Sequence Models

By J V

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Apr 24, 2020

Great Content with real-time example looking forward to doing more courses

By Andrius T

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Nov 2, 2019

Please remove repetitions from videos, really annoying thing. Great job :)

By Luca C

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Oct 13, 2019

Awesome notebooks to gain practical experience with deep learning systems.

By Philippe A

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Oct 21, 2018

This course is very interesting! Again! It requires basic Keras knowlegde.

By Lawrence K

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Jan 4, 2024

the last week on transformers was confusion both as intuition and process

By EZ

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Feb 18, 2018

Course is excellent. Assignment, however, could use some more refinement.

By Sahar N

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Sep 23, 2022

It was really good, but I think I still didn't understand transformers!

By Attili S

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Aug 12, 2020

It would be good to extend some more detailed explanation in this course

By Sebastian M

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Apr 7, 2019

need some reviewing in the optional parts of the programming assignments

By Guan W

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Mar 10, 2018

Excellent course content, but poor maintenance of programming assignment

By Filip V

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Feb 25, 2018

Provides good exposure to sequence models for NLP and speech processing.

By Xirui Z

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Apr 7, 2021

Instructions for labs are not clear enough, especially the layers part.

By Mandeep S G

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Jan 25, 2020

Great exercises but videos were slightly rushed. Overall a good course.

By Vinod C

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Apr 29, 2019

Good course. Feel a little bit rushed. Difficult to retain the concepts

By Chen L

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Mar 14, 2018

The content is great, but the programming exercises are full of errors.

By Xiao

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Mar 8, 2018

Some techniques for keras need to be clarified. Generally a good course

By Jon M

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Jul 7, 2021

I liked this particular set of lectures, too, now on to something new.

By Vamvakaris M

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Sep 8, 2019

It required coding on keras and tensorflow not appropriate introduced.

By Rafael B d S

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Aug 6, 2019

The Course is great! But the programming assignments has too many bugs

By Pete H

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Dec 12, 2018

it's very difficult to submit last programming exercise "trigger word"

By Ishan S

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Jun 27, 2020

More clarification on what we are doing in the programming exercises

By Emanuel G

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Dec 13, 2018

Great introduction to LSTMs, RNNs, GRUs, NLP and speech recognition.

By Nilesh R

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Mar 20, 2018

Great content but I felt it was bit rushed and squeezed in 3 weeks .

By Alex M

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Mar 14, 2018

The quality was a bit down but still very worthwhile and interesting

By Vivek K

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Jul 20, 2018

Great practical experience. Would have preferred a bit more theory.