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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
Author (1):
Sudharsan Ravichandiran Sudharsan Ravichandiran
Profile icon 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 Index

Chapter 2. Face and Audio Recognition Using Siamese Networks

In the last chapter, we learned about what meta learning is and different types of meta learning techniques. We also saw how to learn gradient descent by gradient descent and optimization as a model for few-shot learning. In this chapter, we will learn one of the most commonly used metric-based one-shot learning algorithms called siamese networks. We will see how siamese networks learn from very few data points and how they are used to solve the low data problem. After that we will explore the architecture of siamese networks in detail and we will see some of the applications of siamese networks. At the end of this chapter, we will learn how to build face and audio recognition models using siamese networks.

In this chapter, you will learn the following:

  • What are siamese networks?
  • Architecture of siamese networks
  • Applications of siamese networks
  • Face recognition using siamese networks
  • Building an audio recognition model using siamese...
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