
Mastering Optimization-Based Meta-Learning: Implementing MAML with PyTorch on the MNIST Dataset
Meta-learning, often described as "learning to learn," is a burgeoning area within machine learning. Its key goal is to endow models with the capacity to adapt swiftly to tasks or domains even when there’s scarcity of data. Among the prominent




