Convergence Algorithm, Machine Learning, Gradient Descent, Global Minima, Multiple Linearregression.
/d(θ1) // Since hθ(x)=θ0+θ1x and d hθ(x)/ dθ = d θ0+θ1x/ dθ1 = x J(θ0,θ1) /d(θ1) = 1/m [i =1 -> m Σ (hθ(x)^i - y^i)] * x^i // Final conclusion Repeat until Convergence { θ0 := θ0 - α (1/m [i =1 -> m Σ (hθ(x)^i - y^i)] * 1) θ1 := θ1 - α (1/m [i =1 -> m Σ (hθ(x)^i - y^i)] * x^i) } So this is the