Description: Exponential decay is a method used in the field of machine learning and hyperparameter optimization, which involves gradually reducing a model’s learning rate over time. This approach is fundamental for stabilizing the training process, as it allows the model to make finer adjustments as it approaches an optimal solution. The idea behind exponential decay is that at the beginning of training, a higher learning rate can help the model explore the solution space more effectively, while a lower rate in later stages allows for more precise and controlled convergence. This method is commonly implemented in optimization algorithms like Adam or SGD (Stochastic Gradient Descent), where the learning rate is adjusted based on the epoch or number of iterations. Exponential decay not only improves training stability but can also help avoid issues like overfitting, allowing the model to generalize better to unseen data. In the context of neural networks, especially in complex architectures, exponential decay has become a standard practice for optimizing model performance.
PATROCINADORES
