Optimization

The Optimization category is dedicated to the mathematical core of model training. We explore the complex mechanics of Stochastic Gradient Descent (SGD), the evolution of the Adam optimizer, and the theory of backpropagation through high-dimensional landscapes. Discussions here focus on convergence theory, learning rate schedules, and the mathematical tricks used to prevent vanishing gradients. This section is ideal for those looking to understand how models learn and how to tune the training process for maximum performance and stability.