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• Gradient Descent
  Now we calculate the derivatives and have: or, having calculated the derivatives:
  6 KB (839 words) - 13:40, 30 August 2013
• Method of Least Squares
  * we find partial derivatives to find the critical (minimal in our case) value
  982 B (167 words) - 00:05, 10 February 2014
• Regularization
  ...(\theta)$ by adding the regularization term, we need to change the partial derivatives of $J(\theta)$. So the algorithm now looks as follows:
  5 KB (791 words) - 11:09, 28 August 2013
• Neural Networks
  ''Back Propagation'' is a technique for calculating partial derivatives in neural networks To compute derivatives we use Back Propagation
  16 KB (2,310 words) - 12:44, 23 August 2013
• PROMETHEE/Properties
  * to optimize we take all partial derivatives plus the Lagrangian and equal them to 0:
  9 KB (1,832 words) - 20:55, 9 February 2014
• Positive-Definite Matrices
  So a matrix of second derivatives ([[Hessian Matrix]]) is
  6 KB (867 words) - 00:08, 14 November 2015
• Calculus: Single Variable (coursera)
  * Derivatives -Definition and interpretations of the derivative * Higher derivatives -Definition and interpretation of higher derivatives
  3 KB (420 words) - 22:59, 6 December 2015
• Trigonometric Functions
  == Derivatives and Integrals == === [[Derivatives]] ===
  6 KB (843 words) - 23:21, 6 December 2015
• Hyperbolic Trigonometric Functions
  == Derivatives ==
  2 KB (255 words) - 23:21, 6 December 2015
• Taylor Series
  === [[Derivatives]] === * use the definitions and compute all the Derivatives
  9 KB (1,402 words) - 23:42, 6 December 2015