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


MAML is one of the recently introduced and most popularly used meta learning algorithms and it has created a major breakthrough in meta learning research. Learning to learn is the key focus of meta learning and we know that, in meta learning, we learn from various related tasks containing only a small number of data points and the meta learner produces a quick learner that can generalize well on a new related task even with a lesser number of training samples.

The basic idea of MAML is to find a better initial parameter so that, with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.

So, what do we mean by that? Let's say we are performing a classification task using a neural network. How do we train the network? We will start off with initializing random weights and train the network by minimizing the loss. How do we minimize the loss? We do so using gradient descent. Okay, but how do we use gradient descent for minimizing the loss? We use gradient...