Conclusion and thank you

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En provenance du cours de deeplearning.ai

Sequence Models

8477 notes

deeplearning.ai

Sequence Models

8477 notes

Cours 5 sur 5 dans Specialization Deep Learning

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.

À partir de la leçon

Sequence models & Attention mechanism

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

Conclusion and thank you2:44

Rencontrer les enseignants

Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai

congratulations on making it this far

I just wanna wrap up and leave you with

a few final thoughts

we've been on quite a journey together

but if you've taken the whole

specialization then you've learned about

new networks and deep learning how to

improve deep neural networks of the

structure machine learning projects

convolutional neural networks and then

in this most recent course sequence

models and I know you work really hard

and I also hope you feel very proud of

yourself for your hard work and for how

much you've done so I want to leave you

one

maybe important thought which is that I

think deep learning is a superpower with

deep learning algorithms you can make a

computer see you can have a computer

synthesize novel art or synthesized

music or you can have a computer

translate from one language to another

maybe have it locally radiology image

and render a medical diagnosis or build

pieces of a car that can drive itself

and if that isn't a superpower I don't

know what is and as we wrap up this

sequence of courses as we wrap up this

specialization I hope that you will find

ways to use these ideas to further your

career to pursue your dreams but perhaps

most important to do whatever you think

is the best work you can do our humanity

the world today has challenges but with

the power of a on power of deep learning

I think we can make it a much better

place and now that you have this

superpower I hope you will use it to go

out there and make life better for

yourself but also for other people and

of course I also hope you feel very

proud of your accomplishments in the

power far you've come and of all that

you've learned and when you complete

this sequence of causes you should also

share it on social media like Twitter or

Facebook and let your friends know and

finally the very last thing I want to

say to you is congratulations on

Nikolas I hope you feel great about your

accomplishments but also I want to thank

you very much I know that you have a

busy life but despite that spends a lot

of time watching these videos and maybe

spent a long time also working on the

quizzes and the programming exercises I

hope you enjoyed it and you got a lot

out of the process but I'm also very

grateful for all your time you spend and

for all your hard work you put into

learning these materials so thank you

very much

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