Hands-On One-shot Learning with Python

Hands-On One-shot Learning with Python

Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

person icon Shruti Jadon

ebook icon Packt Publishing

language icon Language - English

updated on icon Updated on Oct, 2020

architecture icon Development,Programming Languages,Python


This eBook includes

Formats : PDF, EPUB, MOBI (Downlodable)

Pages : 156

ISBN : 9781838824877

Language : English

About the Book

Book description

Get to grips with building powerful deep learning models using PyTorch and scikit-learn

Key Features

  • 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

Book 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.

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

Who this book is 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.

Hands-On One-shot Learning with Python

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Author Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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