FXN of Med: Intuit and Explore thru Derivatives

Allowing access to derivative calculus and intuiting derivatives based on coordinate output and exploring medical archives

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Inspiration

Autistic and neurodevelopmentally disabled

What it does

Intuits derivatives and explores both medical papers and derivative calculus

How we built it

Python

Challenges we ran into

Adaptability

Accomplishments that we're proud of

Boiling derivatives down into an input //output and understandable story: Making derivative calculus and thus higher learning accessible

What we learned

Python is a language // facilitator, not an operator.

What's next for FXN of Med: Intuit and Explore thru Derivatives

AI in graduate and all higher learning (cornell.edu)

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Updates

posted an update —

-- coding: utf-8 --

""" Created on Fri Mar 29 22:13:33 2024 @author: Mohammed """ import numpy as np def basic_differentials(a, x, c): step_one = a*c differentiated_power = c-1 step_two = x ** differentiated_power step_three = step_one * step_two return step_three

def chain_rule(x, y, z): t = basic_differentials(1, y, 1) return basic_differentials(x, y, z) * t def product_rule(a, b, c, d, e, f): "a, b, and c as well as d, e, f respectively represent the two values being differentiated" diff_p = basic_differentials(a, b, c) diff_g = basic_differentials(d, e, f) p = a * (b ** c) l = d * (e ** f) five = p * diff_g six = l * diff_p return five + six def quotient_rule(a, b, c, d, e, f): diff_p = basic_differentials(a, b, c) diff_g = basic_differentials(d, e, f) p = a * (b ** c) l = d * (e ** f) five = p * diff_g six = l * diff_p sev = six - five l = l ** 2 return sev / l

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