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


We started off by understanding what meta learning is and how one-shot, few-shot, and zero-shot learning is used in meta learning. We learned that the support set and query set are more like a train set and test set but with k data points in each of the classes. We also saw what n-way k-shot means. Later, we understood different types of meta learning techniques. Then, we explored learning to learn gradient descent by gradient descent where we saw how RNN is used as an optimizer to optimize the base network. Later, we saw optimization as a model for few-shot learning where we used LSTM as a meta learner for optimizing in the few-shot learning setting.

In the next chapter, we will learn about a metric-based meta learning algorithm called the Siamese network and we will see how to use a Siamese network for performing face and audio recognition.