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

About the book

Book Cover

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.

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 and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.

By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.

Get the book


Check out my Deep Reinforcement Learning Repo here.

Awesome Meta Learning Awesome

Check the curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources here.

Table of contents

  • 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
  • 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