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Multivariate (derivative (Jacobian (Screen Shot 2019-05-16 at 5.04.54 PM
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Multivariate
derivative
Jacobian
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direction of the steepest slope of the Jacobian function or direction of steepest ascent
Why the gradient is the direction of steepest ascent?
Partial derivative graph
- \( \frac{\delta f} {\delta x} \)
- \( \frac{\delta f} {\delta y} \)
Divergence
definition
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multivariable functions
function type
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Coursera
Neural Network
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Optimize Cost functions
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Jacobian
Jacobian matrix
NOTE:
- transform from a point in time in distance function (function) to a point in speed function (derivative)
- the derivative is a transform function
is like a linear transformation matrix
Example
- J(0,0,0) = [0,0,3] is a derivative value /vector of length 3 pointing in the 3d dimension
Note:
1 variable -> derivative is a vector of length 1
2 variable -> derivative is a vector of length 2
3 variable -> derivative is a vector of length 3
Definition
- row vector where each entry is the partial derivative of f with respect to each one of those variables in turn
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