Hands-On Meta Learning With Python

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more

Hands-On Meta Learning With Python
Hands-On Meta Learning With Python

Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow. You will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will explore Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. Then, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.

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Table of contents

1. Introduction to Meta Learning

  • 1.1. What is Meta Learning?
  • 1.2. Meta Learning and Few-Shot
  • 1.3. Types of Meta Learning
  • 1.4. Learning to Learn Gradient Descent by Gradient Descent
  • 1.5. Optimization As a Model for Few-Shot Learning

2. Face and Audio Recognition using Siamese Network

3. Prototypical Network and its variants

4. Relation and Matching Networks Using Tensorflow

5. Memory Augmented Networks

6. MAML and its variants

7. Meta-SGD and Reptile ALgorithms

8. Gradient Agreement as an Optimization Objective

9. Recent Advancements and Next Steps

  • 9.1. Task Agnostic Meta Learning
  • 9.2. TAML Algorithm
  • 9.3. Meta Imitation Learning
  • 9.4. MIL Algorithm
  • 9.5. CACTUs
  • 9.6. Task Generation using CACTUs
  • 9.7. Learning to Learn in the Concept Space