2D Mode: Visualize gradient descent on 2D functions. 3D Mode: Explore gradient descent on 3D surfaces. Interactive: Adjust parameters like learning rate, number of iterations, and see how random ...

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We ...

For 1D functions, the program will generate a 2D plot of the function and the optimization path. For 2D functions, a 3D plot of the function's surface and the path taken by the gradient descent ...