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

Learning to learn in concept space

Now we'll see how to learn to learn in the concept space using deep meta learning. First, how do we perform meta learning? We sample a batch of related tasks and some k data points in each task and train our meta learner. Instead of just training using our vanilla meta learning technique, we can combine the power of deep learning with meta learning. So, when we sample a batch of tasks and some k data points in each task, we learn the representations of each of the k data points using deep neural networks and then we'll perform meta learning on those representations.

Our framework consists of three components:

  • Concept generator
  • Concept discriminator
  • Meta learner

The role of the concept generator is to extract the feature representations of each of the data points in our dataset, capturing its high-level concept, and the role of the concept discriminator is to recognize and classify the concepts generated by the concept generator, while the meta learner learns...