Discover Machine Learning By Creating Music

artificial intelligence

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Amazon Web Services Unveils the DeepComposer: A Keyboard for Learning Machine Learning and Generative Antagonist Networks (GAN) While Creating Music …

The Machine Learning is a complex technology, which is not necessarily easy to learn. To help developers get started, Amazon has been multiplying initiatives for several years now.

End of 2018, AWS Launched Free Machine Learning Training. The Seattle firm has also created devices to learn in a fun way, such as the DeepLens camera in 2017 and the DeepRacer race cars in 2018.

This year, as part of the AWS re Invent conference, Amazon has unveiled the third device to discover machine learning while having fun. It’s about DeepComposer: a 32-key and 2-octave keyboard to learn Machine Learning while creating music.

AWS DeepComposer allows you to learn about GANs

More precisely, this keyboard makes it possible to learn about “Generative Artificial Intelligence” to generate creative works using AI. It’s about teaching machines to generate content thanks to generative antagonistic networks.

As a reminder, this approach involves confronting two different neural networks to produce original digital works from samples. In this case, AWS DeepComposer allows you to train and optimize GAN models to create music.

Developers can train their own models, or rely on models pre-trained by Amazon. In both cases, the music created from the template is then edited on the DeepComposer Console via the AWS Cloud and generated. It can then be shared via SoundCloud.

Note that this is only a first announcement and that the DeepComposer is not ready for launch yet.

Introduction of machine learning

There’s a number of terms that get used in machine learning or artificial intelligence (AI) is really the original term to describe a computer systems that exhibit some sort of intelligence machine learning is the notion of a machine actually physically learning to do something as opposed to being programmed machine learning looks at real-world examples of occurrences of causes and effects and it learns to predict those that learn to understand so if you’re using your iPhone and you’re typing a message to somebody and it’s ordering completing that systems watching millions and millions of people’s sentences and is constantly learning to predict what’s going to be said next that’s a machine learning system one example that everybody knows about today is self-driving cars but these cars actually be able to reason about hundreds of other human beings on the road doing complicated random things that might sometimes not even make sense is registered but it’s only now with the sudden power and availability they are that we can begin to deal at some complex data suddenly you know you’re dramatically reducing deaths on words you will suddenly you know extended the availability of transports and more people and that’s truly transformative.

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