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.

Related Content you might be interested in

Current Title:

Small book image
Hands-On One-shot Learning with Python
Shruti Jadon | Ankush Garg
Apr, 2020 | 156 pages
Small book image
Hands-On Meta Learning with Python
Sudharsan Ravichandiran
Dec, 2018 | 226 pages
Small book image
Hands-On Mathematics for Deep Learning
Jay Dawani
Jun, 2020 | 364 pages
Small book image
Advanced Deep Learning with Python
Ivan Vasilev
Dec, 2019 | 468 pages
Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: One-shot Learning Introduction
Section 1: One-shot Learning Introduction
Free Chapter
2
Introduction to One-shot Learning
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
3
Section 2: Deep Learning Architectures
Section 2: Deep Learning Architectures
4
Metrics-Based Methods
Metrics-Based Methods
Technical requirements
Parametric methods – an overview
Understanding Siamese networks
Understanding matching networks
Coding exercise
Summary
Questions
Further reading
5
Model-Based Methods
Model-Based Methods
Technical requirements
Understanding Neural Turing Machines
Memory-augmented neural networks
Understanding meta networks
Coding exercises
Summary
Questions
Further reading
6
Optimization-Based Methods
Optimization-Based Methods
Technical requirements
Overview of gradient descent
Understanding model-agnostic meta-learning
Understanding LSTM meta-learner
Exercises
Summary
Questions
Further reading
7
Section 3: Other Methods and Conclusion
Section 3: Other Methods and Conclusion
8
Generative Modeling-Based Methods
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
9
Conclusions and Other Approaches
Conclusions and Other Approaches
Recent advancements
Related fields
Applications
Further reading
10
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Understanding matching networks

Matching networks, in general, propose a framework that learns a network that maps a small training dataset and tests an unlabeled example in the same embeddings space. Matching networks aim to learn the proper embeddings representation of a small training dataset and use a differentiable kNN with a cosine similarity measure to check whether a test data point has already been seen.

Matching networks are designed to be two-fold:

  • Modeling level: At the modeling level, they propose matching networks, which uses advances made in attention and memory that enable fast and efficient learning.
  • Training procedure: At the training level, they have one condition—the distribution of training and test sets must be the same. For example, this could mean showing a few examples per class and switching the task from minibatch to minibatch, similar to how...
Previous Section
End of Section 5
Next Section