Understanding Optimizers in Deep Learning with 3D Visualizations

Optimizers play a crucial role in deep learning, adjusting model parameters to minimize the loss function and improve performance. Implementing various optimizers using pure Python and NumPy provides valuable insights into their mechanics, from momentum-based updates to adaptive learning rate strategies.

Optimization in 3D

A key aspect of this exploration is visualizing the optimization process on 3D surfaces. By plotting the loss landscape and tracking different optimizers, it becomes clear how each method navigates towards convergence. For example, Stochastic Gradient Descent (SGD) and Adam follow distinct paths, revealing differences in speed and stability.