Hands-On One-shot Learning with Python

What this book covers

Chapter 1, Introduction to One-shot Learning, tells us what one-shot learning is and how it works. It also tells us about the human brain's workings and how it translates to machine learning.

Chapter 2, Metrics-Based Methods, explores methods that use different forms of embeddings, and evaluation metrics, by keeping the core as basic k-nearest neighbors.

Chapter 3, Model-Based Methods, explores two architectures whose internal architectures help to train a k-shot learning model.

Chapter 4, Optimization-Based Methods, explores different forms of optimization algorithms, which help in improving accuracy even when the volume of data is low.

Chapter 5, Generative Modeling-Based Methods, explores the development of a Bayesian ...