Machine Learning, Optimization, Logistic Regression, Data Science, Convex Optimization.
return np.ravel(np.ones(len(Y))*(np.log(1+np.exp(X*beta)))-Y.T*X*beta)[0] def nabla_f(beta): return X.T*(1/(1+1/np.exp(X*beta))-Y) def nabla2_f(beta): return X.T*(np.diag(np.ravel(np.exp(X*beta)/np.power(1+np.exp(X*beta),2)))*X) Implement Gradient Descent Algorithm (λ = 0.001) beta =