👉 Training math in machine learning involves optimizing model parameters to minimize a loss function, which quantifies the discrepancy between predicted and actual outcomes. This process typically uses gradient-based optimization algorithms like Stochastic Gradient Descent (SGD) or its variants, which iteratively adjust parameters in the direction that reduces the loss. The training data is fed into the model multiple times (epochs), with each epoch consisting of a forward pass to compute predictions and a backward pass to calculate gradients. These gradients guide the parameter updates, aiming to find the optimal set of parameters that best fit the training data. Regularization techniques, such as L1 or L2 regularization, and early stopping are often employed to prevent overfitting and ensure the model generalizes well to unseen data.
