Optimization
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This section is important given some models as linear regressiona and logistic regression
based its learning algorithms in this numericall solutions.


why doest work?


   
Slides
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.. list-table:: Notebooks 
    :widths: 10  20 
    :header-rows: 1

    * - Model
      - Material
  
    * - Gradient Descendent
      - `GDS <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Numerical%20optimization/sgd_GradientDescent(ai_page).ipynb>`_
    * - Raphson Newton
      - `Raphson Newton <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Numerical%20optimization/RaphsonNewthon(ai_page).ipynb>`_
    * - Lagrange Multiplier (notebook)
      - `Lagrante utility maximization <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Lagrange/Lagrange(ai-page).ipynb>`_
    * - Grey Wolf Optimizer (GWO) (slides)
      - `GWO <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/GWO.pdf>`_

in this section you find material about numerical, linear and metaheuristics.