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

What are siamese networks?

A siamese network is a special type of neural network and it is one of the simplest and most popularly used one-shot learning algorithms. As we have learned in the previous chapter, one-shot learning is a technique where we learn from only one training example per class. So, a siamese network is predominantly used in applications where we don't have many data points in each class. For instance, let's say we want to build a face recognition model for our organization and about 500 people are working in our organization. If we want to build our face recognition model using a Convolutional Neural Network (CNN) from scratch, then we need many images of all of these 500 people for training the network and attaining good accuracy. But apparently, we will not have many images for all of these 500 people and so it is not feasible to build a model using a CNN or any deep learning algorithm, unless we have sufficient data points. So, in these kinds of scenarios, we can resort...