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Introduction to One-shot Learning

Humans can learn new things with a small set of examples. When presented with stimuli, humans seem to be able to understand new concepts quickly and then recognize variations of those concepts in the future. A child can learn to recognize a dog from a single picture, but a machine learning system needs a lot of examples to learn the features of a dog and recognize them in the future. Machine learning, as a field, has been highly successful at a variety of tasks, such as classification and web searching, as well as image and speech recognition. Often, however, these models do not perform well without a large amount of data (examples) to learn from. The primary motivation behind this book is to train a model with very few examples that is capable of generalizing to unfamiliar categories without extensive retraining.

Deep learning has played an important...

Key benefits

Learn how you can speed up the deep learning process with one-shot learning

Use Python and PyTorch to build state-of-the-art one-shot learning models

Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning

Description

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.

Who is this book for?

If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.

What you will learn

Get to grips with the fundamental concepts of one- and few-shot learning

Work with different deep learning architectures for one-shot learning

Understand when to use one-shot and transfer learning, respectively

Study the Bayesian network approach for one-shot learning

Implement one-shot learning approaches based on metrics, models, and optimization in PyTorch

Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data

Explore various one-shot learning architectures based on classification and regression

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Anoym Customer 1234 Aug 16, 2023

while I can't comment on the material inside yet, the book feels cheap. It feels bad holding it in hand, not sure how else to describe it. the print quality is also poor

Amazon Verified review

Davide Umberto Scalzo Sep 19, 2020

Since I've started getting deeper into machine learning I've put together a bit of a connection of books and this was a big disappointment. Writing style is not great, but whatever..the concerns are as to the poorly explained content and even worse code containing bugs, like the matching network example training on validation data, not well structured etc.. had better luck with blog posts or straight up reading the original papers.

Amazon Customer Jul 01, 2020

This book summarizes all major few-shot learning papers. The authors have done a good job making this an engaging read irrespective of your experience in the subject. Great book to start, if you are new to this field.

M Jun 30, 2020

Good book to start learning about few-shot learning. Have been through few-chapters, the concepts are explained well. It also have a code repo, which has been very helpful.

ConfusedSoul Jun 30, 2020

This is an excellent book to get started with the area of One-Shot learning. My favorite part of the book was the lucid explanation of concepts backed by code snippets. What's also nice is that a lot of thought has been put into the organization of the book's contents itself so a person new to the area of one and few-shot learning isn't overwhelmed. Highly recommend!

Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

What do you get with Print?

Hands-On One-shot Learning with Python

Introduction to One-shot Learning

Technical requirements

The human brain – overview

How the human brain learns

Machine learning – historical overview

One-shot learning – overview

Setting up your environment

Coding exercise

kNN – basic one-shot learning

Summary

Questions

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Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

What do you get with Print?

Key benefits

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Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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