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
Chain Rule: https://youtu.be/cE6wr0_ad8Y
Answer to the question at the end of the video:
∂z/∂y = 2x + 3y^2 + 9x^2
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Links discussed in this video:
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:
Calculus Made Easy: https://www.amazon.ca/Calculus-Made-Silvanus-Phillips-Thompson/dp/1456531980
Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code
p5.js: https://p5js.org/
Processing: https://processing.org
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