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 5. Memory-Augmented Neural Networks

So far, in the previous chapters, we have learned several distance-based metric learning algorithms. We started off with siamese networks and saw how siamese networks learn to discriminate between two inputs, then we looked at prototypical networks and variants of prototypical networks, such as Gaussian prototypical networks and semi-prototypical networks. Going ahead, we explored interesting matching networks and relation networks.

In this chapter, we will learn about Memory-Augmented Neural Networks (MANN), which are used for one-shot learning. Before diving into MANN, we will learn about their predecessor, Neural Turing Machines (NTM). We will learn how NTMs make use of external memory for storing and retrieving information and we will also see how to use a NTM for perform copy tasks.

In this chapter, we will learn about the following:

  • NTM
  • Reading and writing in NTM
  • Addressing mechanisms
  • Copy tasks using NTM
  • MANN
  • Reading and writing in MANN