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


In this chapter, we've learned about Meta-SGD and the Reptile algorithm. We saw how Meta-SGD differs from MAML and how Meta-SGD is used in supervised and reinforcement learning settings. We saw how Meta-SGD learns the model parameter along with learning rate and update direction. We also saw how to build Meta-SGD from scratch. Then, we learned about the Reptile algorithm. We saw how Reptile differs from MAML and how Reptile acts as an improvement over the MAML algorithm. We also learned how to use Reptile in a sine wave regression task.

In the next chapter, we'll learn how we can use gradient agreement as an optimization objective in meta learning.