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Sequence Models

This course is a part of Deep Learning, a 5-course Specialization series from Coursera.

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

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Rating 4.6 based on 734 ratings
Length 4 weeks
Starts Feb 11 (10 weeks ago)
Cost $69
From deeplearning.ai via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

deep learning in 56 reviews

Very interesting application of deep learning.

I think its one of the best deep learning courses out there.

Apologies for flashing this preamble for every course that I review on coursera.This course is the 5th and final one in a 5 part series offered by Dr. Andrew Ng on deep learning on coursera.

I start to understand the way deep learning community deal with NLP, i.e., ingenious design of network structure inspired by the pattern human beings perceive the world.

I hope I can combine deep learning with traditional methods to better understand NLP.

During last three months, his not only taught me deep learning but brought me peace helping me go through a hard time.

awesome This course given me the basic understanding about analyzing sequence data with the help of deep learning.

Firstly, thank to the course instructors and Dr.Ng for teaching us deep learning.

sequence models in 42 reviews

That's what make this course awesome Best course .. really gave me a deep understanding of the sequence models The assignments can be made a little difficult.

Sequence models are significantly harder to understand than regular models.

Another desire left unfulfilled was that I would want the sequence models course doubled in all dimensions, ie lectures, assignments, etc.

Great overview of sequence models.

This course is best in learning sequence models applied in speech and natural processing systems.

Sequence Models, especially attention mechanisms seem to have so much potential.

thank u for helping me understanding RNN/LSTM finally The Sequence Models course was the one I sought out in the deep learning specialization.

However, I expected a lot more details about the sequence models, and recurrent networks as much as the ones given in the previous courses.

highly recommend in 15 reviews

Highly recommend!

I find CNN/RNN courses very helpful, but I would highly recommend to improve the exercises.

very well structured course, awesome lectures and explanations by Prof. Andrew.I'd highly recommend this course and specialization.

Great effort by Prof. Andrew NG, highly recommended.

overal impression : exceptional and highly recommended.

I will highly recommend it.

I highly recommend the whole specialisation for any one serious about DL.

Absolutely highly recommended.The only negative part about this course is that it ends.

speech recognition in 11 reviews

I feel I have a better understanding of how some of the "magic" technology like virtual assistants and speech recognition work.

Still alot of concepts unclear, will revise again Excellent course in order to learn an introduction about Natural Language Processing and Speech Recognition.

Excellent course to learn about speech recognition and translation!

It's a course about a lot of things: speech recognition, music generation, image captioning, machine translation, ecc.It's highly recommended to study previous courses to fully understand the concepts.There are some errors in the exercises because it's a new course.

Great design of the structure to cover both language modeling and speech recognition.

Very Useful course Gives the student an excellent perspective on how to go about word embeddings & speech recognition.

And I have learned many intuitions and skills of Sequence Models and its application in various NLP tasks and speech recognition.

May be extend this to 4 to 5 weeks and spend little more time on speech recognition, music generation and other audio data processing would have helped.Unlike all other earlier modules, this one had many issues with grader and many errors in note book templates.

recurrent neural networks in 10 reviews

a very good course with lots practical assignments which helps me in developing AI with Recurrent Neural Networks and apply word2vecs model.

I believe it is useful to take this course in order and it makes sense to study it as a part of the series, though technically that is not necessary.This is one of the best courses to take if you want to understand the basics of Sequence Models (Recurrent Neural Networks).

I found this course to be more difficult than the others; likely because I had very little prior exposure to recurrent neural networks.

I realize though that deep learning requires a lot of practice and experimentation and completing this course (and specialization) is just a tiny first step .. Learnt many useful techniques and knowledge of Sequence Models and Recurrent Neural Networks.

Very pleasant introduction to the field of recurrent neural networks!

Likely due to the rush of getting this final course out Best course you can find about recurrent neural networks.

I learned many new things about practical applications of recurrent neural networks in this class and found the natural language emphasis to be very useful, particularly for certain problems I have been working on for some time!

Professor Ng's lectures provided intuitive ways to understand the complex recurrent neural networks and how to apply it in real world applications.

real world in 7 reviews

I'm very grateful for the path of discovery that it has provided and excited to continue developing my skills in the real world.

Though there are some minor lost clarifications in the flow, the general learning experience of this course is overwhelmingly practical and relevant to many real world scenarios.

A lot of implements of model in real world.

Lots of real world application based assignments.

Programming assignments require more thoughts but they are real world use cases Excellent course with real examples of applied ML.

NLP and speech recognition intition with real world coding examples.

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Coursera

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deeplearning.ai

Rating 4.6 based on 734 ratings
Length 4 weeks
Starts Feb 11 (10 weeks ago)
Cost $69
From deeplearning.ai via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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