![]() Introduction to Machine LearningĪppendix A. ![]() Ordinary Differential Equation - Boundary Value ProblemsĬhapter 25. Predictor-Corrector and Runge Kutta MethodsĬhapter 23. Ordinary Differential Equation - Initial Value Problems Numerical Differentiation Problem Statementįinite Difference Approximating DerivativesĪpproximating of Higher Order DerivativesĬhapter 22. Least Square Regression for Nonlinear Functions ![]() Least Squares Regression Derivation (Multivariable Calculus) Least Squares Regression Derivation (Linear Algebra) Least Squares Regression Problem Statement Solve Systems of Linear Equations in PythonĮigenvalues and Eigenvectors Problem Statement Linear Algebra and Systems of Linear Equations Errors, Good Programming Practices, and DebuggingĬhapter 14. Inheritance, Encapsulation and PolymorphismĬhapter 10. Variables and Basic Data StructuresĬhapter 7. f(x) plot') (root)=bisection_method(fun, 0,1, 1000) fprintf('\tRoot using Bisection method is $f.Python Programming And Numerical Methods: A Guide For Engineers And ScientistsĬhapter 2. 2-5.C xx=linspace(0,1) yy=fun (xx) fprintf('for the function f(x)=') disp(fun) figure(2) plot (xx,yY) xlabel('x') ylabel('f(x)') title('x vs. f(x) plot') (root)=bisection_method(fun, 0,1,1000) fprintf('\tRoot using Bisection method is $f.\n',root) hold on plot (root, fun(root), 'r*') grid on box on $table for x and fl(x) fprintf('\n\t x\t\t\tf(x)\n') xx=-5:1:5 for i=1:length(xx) rr=root xx(i)*10^-10 fprintf('\t%f (8d*10^-10 )= te\n',root, xx(i), fun(rr)) end function for which root have to find fun= (x) 3. 2-5.C xx=linspace(0,1) yy=fun (xx) fprintf('for the function f(x)=') disp(fun) figure (1) plot (xx, YY) xlabel('x') ylabel('f(x)') title('x vs. %
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