When people talk about artificial intelligence, they usually don t mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.
Reinforcement learning has recently become popular for doing all of that and more.
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn t been until recently that we ve been able to observe first hand the amazing results that are possible.
In 2016 we saw Google s AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Self–driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
Learning about supervised and unsupervised machine learning is no small feat. To date I have over SIXTEEN (16!) courses just on those topics alone.
And yet reinforcement learning opens up a whole new world. As you ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
Courses : 22
Specification: Artificial Intelligence: Reinforcement Learning in Python
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