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 8. Gradient Agreement as an Optimization Objective

In the last chapter, we learned about the Meta-SGD and Reptile algorithm. We saw how Meta-SGD is used to find the optimal parameter, optimal learning rate, and the gradient update direction. We also saw how the Reptile algorithm works and how it is more efficient than MAML. In this chapter, we'll learn how gradient agreement is used as an optimization objective for meta learning. As you saw in MAML, we were basically taking an average of gradients across tasks and updating our model parameter. In gradient agreement algorithm, we'll take a weighted average of gradients to update a model parameter and we'll see how adding weights to the gradient helps us to find the better model parameter. We'll explore exactly how gradient agreement algorithm work in this chapter. Our gradient agreement algorithm can be plugged with both MAML and the Reptile algorithm. We'll also see how to implement gradient agreement in MAML from scratch.

In this...