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 1. Introduction to Meta Learning

Meta learning is one of the most promising and trending research areas in the field of artificial intelligence right now. It is believed to be a stepping stone for attaining Artificial General Intelligence (AGI). In this chapter, we will learn about what meta learning is and why meta learning is the most exhilarating research in artificial intelligence right now. We will understand what is few-shot, one-shot, and zero-shot learning and how it is used in meta learning. We will also learn about different types of meta learning techniques. We will then explore the concept of learning to learn gradient descent by gradient descent where we understand how we can learn the gradient descent optimization using the meta learner. Going ahead, we will also learn about optimization as a model for few-shot learning where we will see how we can use meta learner as an optimization algorithm in the few-shot learning setting.

In this chapter, you will learn about the following:

  • Meta learning
  • Meta learning and few-shot
  • Types of meta learning
  • Learning to learn gradient descent by gradient descent
  • Optimization as a model for few-shot learning