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Hands-On Meta Learning with Python

You're reading from Hands-On Meta Learning with Python Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789534207

Length 226 pages

Edition 1st Edition

Languages

Python

Tools

Meta learning

Concepts

Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (12) Chapters

Preface

1. Introduction to Meta Learning

2. Face and Audio Recognition Using Siamese Networks FREE CHAPTER

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

10. Assessments

11. Other Books You May Enjoy

Leave a review - let other readers know what you think

The architecture of matching networks

The overall flow of matching network is shown in the following diagram and it is different from the image we saw already. You can notice how the support set, , and query set, , are calculated through the embedding functions, and , respectively. As you can see, the embedding function, f, takes the query set along with the support set embeddings as input:

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Authors (1)

Sudharsan Ravichandiran

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.

See other products by Sudharsan Ravichandiran

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