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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

Face recognition using siamese networks

We will understand the siamese network by building a face recognition model. The objective of our network is to understand whether two faces are similar or dissimilar. We use the AT&T Database of Faces, which can be downloaded from here:https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

Once you have downloaded and extracted the archive, you can see the folders s1, s2, up to s40, as shown here:

Each of these folders has 10 different images of a single person taken from various angles. For instance, let's open folder s1. As you can see, there are 10 different images of a single person:

We open and check folder s13:

As we know that siamese networks require input values as a pair along with the label, we have to create our data in such a way. So, we will take two images randomly from the same folder and mark them as a genuine pair and we will take single images from two different folders and mark them as an imposite pair. A sample is...

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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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