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Learner Reviews & Feedback for Sequence Models by DeepLearning.AI

4.8
stars
26,162 ratings
3,085 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

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.

WK
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!

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201 - 225 of 3,055 Reviews for Sequence Models

By Ahammad U

Nov 11, 2020

What an awesome course it was? I have completed my Deep Learning Specialization. It was a about three month journey with Coursera and Andrew Ng. I really miss Andrew. I suppose, I will see you, Andrew Ng, in another Machine Learning Specialization on Coursera course. Till than, I am waiting what will come from you.

By Sanket D

Jun 1, 2020

This course gives an in depth explanation and intuition of RNNs used for learning tasks involving Sequences.

The time required to complete programming assignments takes usually more than an hour to complete than the specified time.

Rest it was a very exciting journey to learn deep learning along with Andrew Ng sir!

By Anne G

Sep 13, 2019

I have thoroughly enjoyed the course from start to end! Each course is well organized, the teacher taught really well, and the programming assignments are very rich with easy to follow guidance, and lots of good libraries / functions that we can leverage / learn from. Thank you very much! Have a wonderful day!

By jaylen w

Nov 8, 2018

Finally I finished the whole series of Deep Learning AI, through which I gained a lot of intuition of deep learning algorithms and its implementation. It's great course to get into this new era especially with a excellent teacher like Andrew who really illustrates the core ideas of deep learning algorithms to me.

By Pavel K

Mar 31, 2018

The last module is awesome as all previous ones. Thank you all guys!

Thank you guys who posted questions, thank you guys who posted answers as well. I appreciate you all. And one more special appreciation to Andrew Ng for this entire course. This course gave me a great knowledge and intuition about Deep Learning.

By Rajan A

Jun 22, 2020

I have been through wonderful journey of learning and implementing deep learning from very scratch. This course really transforms one from caterpillar to butterfly with very minimal pain of calculus and linear algebra. Thanks to Andrew and deeplearning.ai team for providing such a marvelous bundles of knowledge.

By Sardhendu M

Feb 9, 2018

Lots and Lots of knowledge and experience in 3-weeks of class. In Machine Learning terms, this course maximizes the knowledge and experience gained with sequence models by minimizing the time required to complete the course. Lecture videos are very intuitive while assignment projects are very real-world centric.

By Saimur R A

Sep 5, 2020

As always one of the best courses offer by coursera and it was a hell of a ride. I learned about many things like RNN,sequence model,GRU,LSTM,word triggered,word sampling,translation using deep learning algorithm . Andrew did a fantastic job and keep everything simple so that everything can be understandable.

By Matei I

Mar 31, 2019

Really good choice of topics, including state of the art tools like attention and word embeddings. Very useful, especially for those interested in Language Processing applications. However, the videos and assignments need some more careful editing, because there are occasional mistakes, lazy explanations etc.

By Maryam H

Jan 20, 2020

Very great and aspiring course, I learned lots of concepts in this course. I think It would be better If there was a capstone project for the final course of deep learning specialization. It was very great but If I had the opportunity of implementing a project from zero to 100, It would be more than great...

By Александр

Sep 2, 2018

Great course, though not as awesome as other Ng's courses. I think creators became a bit tired closer to the last course in specialization. Anyway, fantastic courses, fantastic specialization, fantastic professor! Thank you very much!! Looking forward to new courses - maybe Reinforcement Learning, GANs? =)

By Shabie I

Feb 18, 2018

Leave it up to Mr. Andrew Ng to explain complex concepts in a very much intuitive manner. The guy is a world apart when it comes to explaining complex concepts. No other MOOC even comes close. Absolutely highly recommended.

The only negative part about this course is that it ends. You don't want it to end.

By J.-F. R

Feb 18, 2020

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.

By Irvin

Feb 8, 2020

I loved this course as wel, I had a little bit less time to advance as steadily as I did with previous courses. So I had a little bit of trouble getting the context back when starting again each time. The next steps for me are putting what I learned into practice and basing myself on what I learned here.

By Pavel K

Feb 5, 2019

This course offers a great introduction to the models: RNN, GRU and LSTM.

In addition, it illustrates the power of "Word Embedding" and "Attention Model".

The programming assignments are interesting, provide deeper understanding of the models, and show how simple it is to implement these models in Keras.

By Md A A A

Oct 4, 2020

The course content is very good. This course seemed a little bit harder than other previous courses. There is lots of improvement opportunities for Coursera's Lab (Jypeter notebooks), every now and then it does not work i.e. cant submit the assignment, it's very very slow, its just not up to the mark.

By Akshay M P

Sep 29, 2020

A very well organized introduction to the wonderland of Recurrent Neural Networks. The course takes the student through multiple use cases of various RNN architectures with practical advice on how and where to use them. The awesome programming assignments with excellent documentation is a highlight!

By CAMILO G Z

Jan 26, 2020

With this course it's possible to conclude that recurrent neural networks are the most powerful variant of neural networks. Used in a many places, this course expleains ingishtfully how they work, what's the math behind them, and how different implementations function for different areas of research.

By Angad S

Feb 16, 2018

Andrew Ng knows how to make people understand the problem, architect a solution and then map it to data. He does it very well. I am going to repeat his course again as I still believe there is something that I have missed and I need to follow him thoroughly. I want to understand and process like him.

By Xiang J

Nov 9, 2019

overall it is another great course, clear explain on the RNN, LSTM, GRU, etc. really like the assignment to implement RNN from scratch. assignments related to Keras needs "googling" outside resources, and there is still some keras homework to be done in order to fully understand the assignment code.

By Jampana b

Feb 14, 2018

Thank you very much instructors. I learnt both fundamentals of deep learning and application of them in simple and efficient way. I have been long and fruitful journey with Andrew Ng. I learnt sound mathematics required for deep learning, tensorflow software, applications of sequence and RNN models.

By 邓佳阳

Jun 8, 2020

非常感谢老师提供的课程,原本课程实验对LSTM有相关研究需求,该课程很关键的提供了相关知识点的教学,再次感谢老师和平台!

Thank you very much for the course provided by the teacher. The original course experiment has relevant research needs for LSTM. This course provides the teaching of relevant knowledge points. Thank you again for the teacher and platform!

By Pawan S S

Jan 8, 2021

A very good course to learn the fundamentals of Sequence models. It contain a lot of important developments of the sequence models and together with the programming assignments, it makes easier to learn. I found this course very easy to follow and understand the theories. I highly recommend this.

By TANVEER M

Aug 25, 2019

I have always found difficult how RNN and LSTM works as theretically I was not getting a clear picture how it was working .The programming assignments helped clear my doubts and I got a clear understanding to a lot of extent how this mechanism is working and how it is useful in speech synthesis.

By Zhiming C

Jun 14, 2020

This course introduces the basic idea of RNN, GRU and LSTM models. They are obviously harder than the CNN models and the concepts are not so easy to understand. Thanks to the systematic introduction! Together wit the excises I can understand better the theory from the applications. It's great!