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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 9: Recent Advancements and Next Steps

  1. Different types of inequality measures are Gini coefficients, the Theil index, and the variance of algorithms.
  2. The Theil index is the most commonly used inequality measure. It's named after a Dutch econometrician, Henri Theil, and it's a special case of the family of inequality measures called generalized entropy measures. It can be defined as the difference between the maximum entropy and observed entropy.
  3. If we enable our robot to learn by just looking at our actions, then we can easily make the robot learn complex goals efficiently and we don't have to engineer complex goal and reward functions. This type of learning—that is, learning from human actions—is called imitation learning, where the robot tries to mimic human action.
  4. A concept generator is used to extract features. We can use deep neural...
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