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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 147
Veröffentlichungsjahr: 2020
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Hands-On One-shot Learning with Python
Copyright © 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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Commissioning Editor:Amey VarangaonkarAcquisition Editor:Yogesh DeokarContent Development Editor:Athikho Sapuni RishanaSenior Editor: Sofi RogersTechnical Editor: Manikandan KurupCopy Editor: Safis EditingProject Coordinator: Aishwarya MohanProofreader: Safis EditingIndexer: Priyanka DhadkeProduction Designer: Jyoti Chauhan
First published: April 2020
Production reference: 1090420
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-83882-546-1
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About the authors
Shruti Jadon is currently working as a Machine Learning Software Engineer at Juniper Networks, Sunnyvale and visiting Researcher at Rhode Island Hospital (Brown University). She has obtained her master's degree in Computer Science from University of Massachusetts, Amherst. Her research interests include deep learning architectures, computer vision, and convex optimization. In the past, she has worked at Autodesk, Quantiphi, SAP Labs, and Snapdeal.
Ankush Garg is currently working as a Software Engineer in the auto-translation team at Google, Mountain View. He has obtained his master's degree in Computer Science from the University of Massachusetts, Amherst, and his bachelor's at NSIT, Delhi. His research interests include language modeling, model compression, and optimization. In the past, he has worked as a Software Engineer at Amazon, India.
About the reviewer
Greg Walters has been involved with computers and computer programming since 1972. He is well versed in Visual Basic, Visual Basic.NET, Python, and SQL, and is an accomplished user of MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous individuals in many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is currently retired and, in his spare time, is a musician and loves to cook. He is also open to working as a freelancer on various projects.
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Table of Contents
Title Page
Copyright and Credits
Hands-On One-shot Learning with Python
About Packt
Why subscribe?
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: One-shot Learning Introduction
Introduction to One-shot Learning
Technical requirements
The human brain – overview
How the human brain learns
Comparing human neurons and artificial neurons
Machine learning – historical overview
Challenges in machine learning and deep learning
One-shot learning – overview
Prerequisites of one-shot learning
Types of one-shot learning
Setting up your environment
Coding exercise
kNN – basic one-shot learning
Summary
Questions
Section 2: Deep Learning Architectures
Metrics-Based Methods
Technical requirements
Parametric methods – an overview
Neural networks – learning procedure
Visualizing parameters
Understanding Siamese networks
Architecture
Preprocessing
Contrastive loss function
Triplet loss function
Applications
Understanding matching networks
Model architecture
Training procedure
Modeling level – the matching networks architecture
Coding exercise
Siamese networks – the MNIST dataset
Matching networks – the Omniglot dataset
Summary
Questions
Further reading
Model-Based Methods
Technical requirements
Understanding Neural Turing Machines
Architecture of an NTM
Modeling 
Reading 
Writing
Addressing
Memory-augmented neural networks
Reading
Writing
Understanding meta networks
Algorithm of meta networks
Algorithm
Coding exercises
Implementation of NTM
Implementation of MAAN
Summary
Questions
Further reading
Optimization-Based Methods
Technical requirements
Overview of gradient descent
Understanding model-agnostic meta-learning
Understanding the logic behind MAML
Algorithm
MAML application – domain-adaptive meta-learning
Understanding LSTM meta-learner
Architecture of the LSTM meta-learner
Data preprocessing
Algorithm – pseudocode implementation
Exercises
A simple implementation of model-agnostic meta-learning
A simple implementation of domain-adaption meta-learning
Summary
Questions
Further reading
Section 3: Other Methods and Conclusion
Generative Modeling-Based Methods
Technical requirements
Overview of Bayesian learning
Understanding directed graphical models
Overview of probabilistic methods
Bayesian program learning
Model
Type generation
Token generation
Image generation
Discriminative k-shot learning
Representational learning
Probabilistic model of the weights
Choosing a model for the weights
Computation and approximation for each phase 
Phase 1 – representation learning
Phase 2 – concept learning
Phase 3 – k-shot learning
Phase 4 – k-shot testing
Summary
Further reading
Conclusions and Other Approaches
Recent advancements
Object detection in few-shot domains
Image segmentation in few-shot domains
Related fields
Semi-supervised learning
Imbalanced learning 
Meta-learning
Transfer learning
Applications
Further reading
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
One-shot learning has been an active field of research for many scientists who are trying to find a cognitive machine that is as close to human beings as possible in terms of learning. As there are various theories as to how humans effect one-shot learning, there are a variety of different methods available to achieve this, ranging from non-parametric models and deep learning architectures to probabilistic models.
Hands-On One-shot Learning with Python will focus on designing and learning about models that can learn information relating to an object from one, or only a few, training examples. The book will begin by giving you a brief overview of deep learning and one-shot learning to get you started. Then, you will learn different methods to achieve this, including non-parametric models, deep learning architectures, and probabilistic models. Once you are well versed in the core principles, you will explore some of the practical real-world examples and implementations of one-shot learning using scikit-learn and PyTorch.
By the end of the book, you will be familiar with one-shot and few-shots learning methods and be able to accelerate your deep learning processes with one-shot learning.
Who this book is for
AI researchers, as well as machine learning and deep learning experts who wish to apply one-shot learning to reduce the overall training time of their models, will find this book to be a very good introductory source of learning.
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 learning framework based on representing object categories with probabilistic models.
Chapter 6, Conclusions and Other Approaches, goes through certain aspects of architecture, metrics, and algorithms to understand how we can determine whether an approach is close to human brain capability.
To get the most out of this book
Knowledge of basic machine learning and deep learning concepts and the underlying math, as well as some exposure to Python programming, will be required for this book.
Software/Hardware covered in this book
OS requirements
Software: Jupyter Notebook, Anaconda
Language and Libraries: Python 3.X and above, PyTorch 1.4, Scikit-learn.
Any OS (Linux environment is preferable).
Hardware: None. But if you wish to increase the speed of training. You can use the same codes with minor modifications on GPU Hardware.
If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
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Select the
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Enter the name of the book in the
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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-One-shot-Learning-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781838825461_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Import the iris dataset."
A block of code is set as follows:
# import small dataset
iris
datasets
.
load_iris
()
X
iris
.
data
y
iris
.
target
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
[2 1 2 1 2 0 1 0 0 0 2 1 1 0 0 0 2 2 1 2 1 0 0 1 2 0 0 2 0 0]
Any command-line input or output is written as follows:
pip install -r requirements.txt
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "We can use a new loss function known as triplet loss, which helps the architecture to get better results."
Get in touch
Feedback from our readers is always welcome.
General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].
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Section 1: One-shot Learning Introduction
Deep learning has brought about a major change to industry—be it manufacturing, medical, or human resources. With this major revolution and proof of concept, almost every industry is trying to adapt its business model to comply with deep learning, but it has some major requirements that may not fit every business or industry. After reading this section, you will have a proper understanding of the pros and cons of deep learning.
This section comprises the following chapter:
Chapter 1
,
Introduction to One-shot Learning
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 role in the advancement of machine learning, but it also requires large datasets. Different techniques, such as regularization, can reduce overfitting in low-data regimes, but do not solve the inherent problem that comes with fewer training examples. Furthermore, the large size of datasets leads to slow learning, requiring many weight updates using gradient descent. This is mostly due to the parametric aspect of an ML algorithm, in which training examples need to be slowly learned. In contrast, many known non-parametric models such as nearest neighbor do not require any training, but performance depends on a sometimes arbitrarily chosen distance metric such as the L2 distance. One-shot learning is an object categorization problem in computer vision. While most ML-based object categorization algorithms require hundreds or thousands of images and very large datasets to train on, one-shot learning aims to learn information about object categories from one, or only a few, training images. In this chapter, we will learn about the basics of one-shot learning and explore its real-world applications.
The following topics will be covered in this chapter:
The human brain—overview
Machine learning—history overview
One-shot learning—overview
Setting up your environment
Coding exercise
