Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

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.
Table of Contents (17 chapters)
Title Page
About Packt

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 nets that are parameterized by some parameter, 
    , to generate the concepts. For examples, our concept generator can be a CNN if our input is an image.
  5. We sample a batch of tasks from the task distributions, learn their concepts via the concept generator, perform meta learning on those concepts, and then we compute the meta learning loss: