In the previous chapter, we learned about the Neural Turing Machine (NTM) and how it stores and retrieves information from the memory. We also learned about the variant of NTM called the memory-augmented neural network, which is extensively used in one-shot learning. In this chapter, we will learn one of the interesting and most popularly used meta learning algorithms called Model Agnostic Meta Learning (MAML). We will see what model agnostic meta learning is, and how it is used in a supervised and reinforcement learning settings. We will also learn how to build MAML from scratch and then we will learn about Adversarial Meta Learning (ADML). We will see how ADML is used to find a robust model parameter. Following that we will learn how to implement ADML for the classification task. Lastly, we will learn about Context Adaptation for Meta Learning (CAML).
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