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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

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789534207
Length 226 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (12) Chapters Close

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

Chapter 5: Memory-Augmented Neural Networks

  1. NTM is an interesting algorithm that has the ability to store and retrieve information from memory. The idea of NTM is to augment the neural network with external memory—that is, instead of using hidden states as memory, it uses external memory to store and retrieve information. 
  2. The controller is basically a feed-forward neural network or recurrent neural network. It reads from and writes to memory.
  3. The read head and write head are the pointers containing addresses of the memory that it has to read from and write to.
  4. The memory matrix or memory bank, or simply the memory, is where we will store the information. Memory is basically a two-dimensional matrix composed of memory cells. The memory matrix contains rows and M columns. Using the controller, we access the content from the memory...
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