The paper introduces a model-agnostic meta-learning (MAML) algorithm designed to enable fast adaptation of deep neural networks to new tasks with minimal training data. MAML is applicable to various learning problems, including classification, regression, and reinforcement learning. The key idea is to train the model's parameters such that a small number of gradient steps with a small amount of new data can produce good generalization performance. This approach optimizes the model for easy fine-tuning, allowing it to adapt quickly to new tasks. The algorithm is evaluated on several benchmarks, showing state-of-the-art performance in few-shot image classification, good results in few-shot regression, and accelerated fine-tuning in reinforcement learning with neural network policies. The paper also discusses the theoretical underpinnings of MAML, its advantages over existing methods, and its potential for future research.The paper introduces a model-agnostic meta-learning (MAML) algorithm designed to enable fast adaptation of deep neural networks to new tasks with minimal training data. MAML is applicable to various learning problems, including classification, regression, and reinforcement learning. The key idea is to train the model's parameters such that a small number of gradient steps with a small amount of new data can produce good generalization performance. This approach optimizes the model for easy fine-tuning, allowing it to adapt quickly to new tasks. The algorithm is evaluated on several benchmarks, showing state-of-the-art performance in few-shot image classification, good results in few-shot regression, and accelerated fine-tuning in reinforcement learning with neural network policies. The paper also discusses the theoretical underpinnings of MAML, its advantages over existing methods, and its potential for future research.