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Hands-On Meta Learning with Python

You're reading from Hands-On Meta Learning with Python

Product type Book

Published in Dec 2018

Publisher Packt

ISBN-13 9781789534207

Pages 226 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

Table of Contents (17) Chapters

Title Page

Dedication

About Packt

Contributors

Preface

1. Introduction to Meta Learning

2. Face and Audio Recognition Using Siamese Networks

3. Prototypical Networks and Their Variants

4. Relation and Matching Networks Using TensorFlow

5. Memory-Augmented Neural Networks

6. MAML and Its Variants

7. Meta-SGD and Reptile

8. Gradient Agreement as an Optimization Objective

9. Recent Advancements and Next Steps

1. Assessments

2. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

Chapter 4. Relation and Matching Networks Using TensorFlow

In the last chapter, we learned about prototypical networks and how variants of prototypical networks, such as Gaussian prototypical and semi-prototypical networks, are used for one-shot learning. We have seen how prototypical networks make use of embeddings to perform classification tasks.

In this chapter, we will learn about relation networks and matching networks. First, we will see what a relation network is and how it is used in one-shot, few-shot, and zero-shot learning settings, after which we will learn how to build a relation network using TensorFlow. Later in this chapter, we will learn about matching networks and how they are used in few-shot learning. We will also see different types of embedding functions used in matching networks. At the end of this chapter, we will see how to build matching networks in Tensorflow.

In this chapter, we will learn about the following:

Relation networks
Relation networks in one-shot, few-shot...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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