Hands-On Meta Learning with Python explains the fundamentals of meta learning and helps you to understand the concept of learning to learn. You will go through various one-shot learning algorithms, such as siamese, prototypical, relation, and memory-augmented networks, and implement them in TensorFlow and Keras. You will also learn about the state-of-the-art meta learning algorithms, such as model-agnostic meta learning (MAML), Reptile, and fast context adaptation via meta learning (CAML). You will then explore how to learn quickly with meta-SGD and discover how to perform unsupervised learning using meta learning.