- Prototypical networks are simple, efficient, and one of the most popularly used few-shot learning algorithms. The basic idea of the prototypical network is to create a prototypical representation of each class and classify a query point (new point) based on the distance between the class prototype and the query point.
- We compute embeddings for each of the data points to learn the features.
- Once we learn the embeddings of each data point, we take the mean embeddings of data points in each class and form the class prototype. So, a class prototype is basically the mean embeddings of data points in a class.
- In a Gaussian prototypical network, along with generating embeddings for the data points, we add a confidence region around them, which is characterized by a Gaussian covariance matrix. Having a confidence region helps to characterize the quality of individual data points, and it is useful with noisy and less homogeneous data.
- Gaussian prototypical networks differ from vanilla prototypical networks in that in a vanilla prototypical network, we learn only the embeddings of a data point, but in a Gaussian prototypical network, along with learning embeddings, we also add a confidence region to them.
- The radius and diagonal are the different components of the covariance matrix used in a Gaussian prototypical network.
Hands-On Meta Learning with Python
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Hands-On Meta Learning with Python
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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.
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Current Title:
Hands-On Meta Learning with Python
Table of Contents (17 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
Free Chapter
Introduction to Meta Learning
Face and Audio Recognition Using Siamese Networks
Prototypical Networks and Their Variants
Relation and Matching Networks Using TensorFlow
Memory-Augmented Neural Networks
MAML and Its Variants
Meta-SGD and Reptile
Gradient Agreement as an Optimization Objective
Recent Advancements and Next Steps
Assessments
Other Books You May Enjoy
Index
Customer Reviews