Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
Table of Contents (17 chapters)
Title Page
About Packt

Chapter 6. MAML and Its Variants

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 aboutContext Adaptation for Meta Learning (CAML).

In this chapter, you will learn about the following:

  • MAML
  • MAML algorithm
  • MAML in supervised and reinforcement...