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 started off with prototypical networks, and we saw how a prototypical network computes the class prototype using the embedding function and predicts the class label of the query set by comparing the Euclidean distance between the class prototype and query set embeddings. Following this, we experimented with a prototypical network by performing classification on an omniglot dataset. Then, we learned about the Gaussian prototypical network, which, along with the embeddings, also uses the covariance matrix to compute the class prototype. Following this, we explored semi-prototypical networks, which are used to handle semi-supervised classes. In the next chapter, we will learn about relation and matching networks.