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 gradient agreement algorithm. We've seen how the gradient agreement algorithm uses a weighted gradient to find the better initial model parameter,

. We also saw how these weights are proportional to the inner product of the gradients of a task and an average of gradients of all of the tasks in a sampled batch of tasks. We also explored how the gradient agreement algorithm can be plugged with both MAML and the Reptile algorithm. Following this, we saw how to find the optimal parameter

in a classification task using a gradient agreement algorithm.

In the next chapter, we'll learn about some of the recent advancements in meta learning such as task agnostic meta learning, learning to learn in the concept space, and meta imitation learning.