Hands-On Meta Learning with Python: Meta learning using one-shot learning,MAML,Reptile,and Meta-SGD with TensorFlow
Authors: Sudharsan Ravichandiran
ISBN-10: 1789534208
ISBN-13: 9781789534207
Publication Date 出版日期: 2018-12-31
Print Length 页数: 226 pages
Book Description
By finelybook
Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks
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.
What you will learn
Understand the basics of meta learning methods,algorithms,and types
Build voice and face recognition models using a siamese network
Learn the prototypical network along with its variants
Build relation networks and matching networks from scratch
Implement MAML and Reptile algorithms from scratch in Python
Work through imitation learning and adversarial meta learning
Explore task agnostic meta learning and deep meta learning
contents
1 Introduction to Meta Learning
2 Face and Audio Recognition Using Siamese Networks
3 Prototypical Networks and Their Variants
4 Relation and Matching Networks Using TensorFlow
5 Memory-Augmented Neural Networks
6 MAML and Its Variants
7 Meta-SGD and Reptile
8 Gradient Agreement as an Optimization Objective
9 Recent Advancements and Next Steps
