Book Image

Hands-On One-shot Learning with Python

By : Shruti Jadon, Ankush Garg
Book Image

Hands-On One-shot Learning with Python

By: Shruti Jadon, Ankush Garg

Overview of this book

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.
Table of Contents (11 chapters)
1
Section 1: One-shot Learning Introduction
3
Section 2: Deep Learning Architectures
7
Section 3: Other Methods and Conclusion

Metrics-Based Methods

Deep learning has successfully achieved state-of-the-art performance in a variety of applications, such as image classification, object detection, speech recognition, and so on. But deep learning architectures often fail when forced to make predictions about data for which there is little supervised information available. As we know, mathematics is fundamental to all machine learning and deep learning models; we convey our data and objectives to machines using mathematical representations of the data. These representations can have many forms, especially if we want to learn complex tasks (for example, disease detection), or if we want our architecture to learn representations based on different objectives, for example, to calculate the similarity between two images, we can calculate both Euclidean distances and cosine similarity.

In this chapter, we will...