Introduction
There are many things which an AI model can do. The generation of music is also one of them; with the use of advanced algorithms, we can make music which seems like a great musician coming from the AI model. There is a class of AI called generative AI, which uses pre-existing patterns in the data provided and tries to create the patterns again with the help of machine learning.
Let's understand more about using Deep Composer and how it works.
How does it work?
Supervised Machine Learning is the most extensively used AI. In this case, we give the computer a set of labelled data (actual examples of questions and correct answers). We expect it to be able to guess the answers to "questions" it hasn't seen before once it has been trained. This can be used for everything from recognizing kitten photographs to estimating our inventory requirements for the coming quarter.
Reinforcement Learning is another type of AI. This is the same type of machine learning that was employed in AWS DeepRacer. You give the computer a limited number of actions to complete, and then reward (or punish) is based on how well each activity helped it achieve a specific objective. It's often utilized in video games, but it also has uses in robotics, marketing, and recommendation systems.
Unsupervised Machine Learning is the final big ML area, and this is where we get Generative AI. Unsupervised learning can be divided into two categories. The first takes a data set and attempts to classify it into groups. It searches the data for patterns. Everything from client segmentation to anomaly detection can benefit from this. Generic AI takes it a step farther. It doesn't only look for patterns; it also attempts to imitate them. In an ideal world, we would develop material that is unrecognizable from the original dataset – not a carbon copy, but something entirely new. We want computers to be imaginative.
As a result, the deep composer creates music using generative AI.
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