Conclusion and thank you

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En provenance du cours de deeplearning.ai

Sequence Models

14475 notes

deeplearning.ai

Sequence Models

14475 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
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of 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
Computer Science

Congratulations on making it this far.

I just want to 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 neural networks and deep learning,

how to improve deep neural networks,

how to structure machine learning projects,

convolutional neural networks, and then in this most recent course, sequence models.

I know you've worked 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 thoughts,

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 synthesize music.

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 it is.

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 dream,

but perhaps most important,

to do whatever you think is the best of what you can do of humanity.

The world today has challenges,

but with the power of AI and power of deep learning,

I think we can make it a much better place.

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.

Of course, I also hope you feel very proud of your accomplishments,

and of how far you've come,

and of all that you've learned.

When you complete this sequence of courses,

you should also share it on

social media like Twitter or Facebook and let your friends know.

Finally, the very last thing I want to say to you

is congratulations on finishing the course.

I hope you feel great about your accomplishments,

but also 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 spend a lot of time

also working on the quizzes and the programming exercises.

I hope you enjoyed it and you've got a lot of the process,

but I'm also very grateful for all your time you spent

and for all your hard work you put into learning these materials.

So, thank you very much.

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