Adaptive Optimization

Adaptive optimization refers to techniques used to improve the training of machine learning models by adjusting how the model learns as it trains. Unlike fixed methods, adaptive optimization algorithms modify learning rates and parameter updates based on data patterns encountered during training. This approach helps the model converge faster and potentially achieve better accuracy by dynamically responding to the complexity of the task, leading to more efficient and effective learning processes.