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Home education Supervised Learning: Crash Course AI #2

Supervised Learning: Crash Course AI #2

Today we’re going to teach John Green Bot how to tell the difference between donuts and bagels using supervised learning! Supervised learning is the process of learning WITH training labels, and is the most widely used kind of learning with it comes to AI – helping with stuff like tagging photos on Facebook and filtering spam from your email. We’re going to start small today and show how just a single neuron (or perceptron) is constructed, and explain the differences between precision and recall. Next week, we’ll build our first neural network.

Read more about the perceptron and update rule here: https://jontysinai.github.io/jekyll/update/2017/11/11/the-perceptron.html

Crash Course is produced in association with PBS Digital Studios.
https://www.youtube.com/pbsdigitalstudios

Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse

Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore

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#CrashCourse #ArtificialIntelligence #MachineLearning

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