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Sequence Models,

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À propos de ce cours

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. 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....

Meilleurs avis

par JY

Oct 30, 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.

par SD

Sep 28, 2018

Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.

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1,526 avis

par Jingxiao Zhang

May 21, 2019

Very useful for understanding what 'sequential' means in DL. I expected as some new solution to the time series scenarios but it turns out a different area from the quantative techniques. It might be helpful applied to more 'sequential' areas.

par Juri Pro

May 20, 2019

Great course!

par Suraj S Jain

May 20, 2019

Simplified content delivered in just the right way to give a perfect intuition of the complex concepts. Really enjoyed doing the whole course.

par Ravi Kiran Savirigana

May 20, 2019

Could have been more thorough like previous courses

par Ayon Banerjee

May 19, 2019

Some handy applications covered. I really enjoyed it.

par Paolo Sanfilippo

May 19, 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.

par Subhadeep Dash

May 19, 2019

Excellent course, helped me a lot in understanding RNNs and LSTMs

par Shilin Sergey

May 18, 2019

Feels again like authors tried to put everything into just a couple of weeks, thus the course turned out to be messy with lots of details hidden. Even though there was a lot to learn, I am still not sure if I understand correctly how to build a simple sequence model.

par Zebin Chen

May 18, 2019

This course is hard to learn. It requires a lot of basic concepts about NLP and RNN. Fortunately, programming exercises have effectively deepened my understanding of these concepts.

par Erick Xavier Zúniga Vásquez

May 18, 2019

The programming assingments have some errors.