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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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Toc

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

NTM


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 an external memory—that is, instead of using hidden states as a memory, it uses an external memory for storing and retrieving the information. The architecture of NTM is shown in the following figure:

The important components of the NTM are as follows:

  • Controller: This is basically a feedforward neural network or recurrent neural network. It reads from and writes to the memory.
  • Memory: 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 N rows and M columns. Using the controller, we access the content from the memory. So, the controller receives input from the external environment and emits responses by interacting with the memory matrix.
  • Read and write heads: The read head and write head are the...
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