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Table Of Contents

Hands-On One-shot Learning with Python

By : Jadon, Garg

4 (7)

Buy this Book

Hands-On One-shot Learning with Python

4 (7)

By: Jadon, Garg

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: One-shot Learning Introduction

Free Chapter

Introduction to One-shot Learning

Technical requirements

The human brain – overview

Machine learning – historical overview

One-shot learning – overview

Setting up your environment

Coding exercise

Summary

Questions

Section 2: Deep Learning Architectures

Metrics-Based Methods

Technical requirements

Parametric methods – an overview

Understanding Siamese networks

Understanding matching networks

Coding exercise

Summary

Questions

Further reading

Model-Based Methods

Technical requirements

Understanding Neural Turing Machines

Memory-augmented neural networks

Understanding meta networks

Coding exercises

Summary

Questions

Further reading

Optimization-Based Methods

Technical requirements

Overview of gradient descent

Understanding model-agnostic meta-learning

Understanding LSTM meta-learner

Exercises

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

Discriminative k-shot learning

Summary

Further reading

Conclusions and Other Approaches

Recent advancements

Related fields

Applications

Further reading

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