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 have learned how to find the optimal model parameter θ that is generalizable across tasks so that we can take fewer gradient steps and learn quickly on the new related tasks. We started off with MAML and we saw how MAML performs meta optimization to calculate the optimal model parameter. Next, we saw adversarial meta learning where we used both clean and adversarial samples for finding the robust initial model parameter. Later, we learned about CAML and we saw how it uses two different parameters, one for learning within the task and one for updating the model parameter.

In the next chapter, we will learn about meta-SGD and Reptile algorithm, which is again used for finding the better initial parameter of a model.