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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

Meta imitation learning


If we want our robot to be more generalist and to perform various tasks, then our robots should learn quickly. But how can we enable our robots to learn quickly? Well, how do we humans learn quickly? Don't we easily learn new skills by just looking at other individuals? Similarly, 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. A robot doesn't really have to learn only from human actions; it can also learn from another robot performing a task or a video of a human/robot performing a task.

But imitation learning is not as simple as it sounds. A robot will take a lot of time and demonstrations to learn the goal and to identify the right policy. So, we'll augment the robot with prior experience...

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