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 ...

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 ...

3.1. The normalized mutual information-gradient difference. In the 2D–3D image registration process, different similarity metric functions have different abilities to capture image information, and ...

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 ...

The efficacy and reliability of segmentation models heavily rely on the activation functions employed within deep learning architectures. However, a persistent challenge lies in the inability of ...