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Hands-On One-shot Learning with Python

You're reading from   Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781838825461
Length 156 pages
Edition 1st Edition
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Authors (2):
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Ankush Garg Ankush Garg
Author Profile Icon Ankush Garg
Ankush Garg
Shruti Jadon Shruti Jadon
Author Profile Icon Shruti Jadon
Shruti Jadon
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Toc

Table of Contents (11) Chapters Close

Preface 1. Section 1: One-shot Learning Introduction
2. Introduction to One-shot Learning FREE CHAPTER 3. Section 2: Deep Learning Architectures
4. Metrics-Based Methods 5. Model-Based Methods 6. Optimization-Based Methods 7. Section 3: Other Methods and Conclusion
8. Generative Modeling-Based Methods 9. Conclusions and Other Approaches 10. Other Books You May Enjoy

Coding exercises

In this section, we will first go through the implementation of NTMs and later go through MAANs using the Omniglot dataset. So, let's begin!

Some parts of the code aren't included as part of this exercise. If you wish to get a runnable code, please take a look at this book's GitHub repository at https://github.com/PacktPublishing/Hands-On-One-shot-Learning-with-Python.

Implementation of NTM

As discussed, an NTM is composed of two important components:

  • A neural network, also known as the controller
  • A two-dimensional matrix called memory

In this tutorial, we will implement a simplistic version of both and try to showcase the copy tasks.

The task objective is as follows:

  • The NTM model is shown...
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