3.5c: Calculus: Partial Derivative - Intelligence and Learning - YoutubeRandom

## 3.5c: Calculus: Partial Derivative - Intelligence and Learning

This video is the third (and final, for now) in a short series on calculus concepts as background for the Gradient Descent algorithm. This video covers the concept of a "partial derivative".

This video is part of session 3 of my Spring 2017 ITP "Intelligence and Learning" course (https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression)

Videos on Calculus:
Power Rule: https://youtu.be/IKb_3FJtA1U

Answer to the question at the end of the video:
∂z/∂y = 2x + 3y^2 + 9x^2

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Contact:
The Coding Train website: http://thecodingtrain.com/

Session 3 of Intelligence and Learning: https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression
Nature of Code: http://natureofcode.com/
Linear Regression on Wikipedia: https://en.wikipedia.org/wiki/Linear_regression

Book discussed in this video:

Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code

p5.js: https://p5js.org/
Processing: https://processing.org

For More Intelligence and Learning: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YJ3XfHhT2Mm4Y5I99nrIKX

This site provides links to random videos hosted at YouTube, with the emphasis on random.

The original idea for this site actually stemmed from another idea to provide a way of benchmarking the popularity of a video against the general population of YouTube videos. There are probably sites that do this by now, but there wasn’t when we started out. Anyway, in order to figure out how popular any one video is, you need a pretty large sample of videos to rank it against. The challenge is that the sample needs to be very random in order to properly rank a video and YouTube doesn’t appear to provide a way to obtain large numbers of random video IDs.

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