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.

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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
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Understanding Neural Turing Machines

During the early days of AI, the field was heavily dominated by a symbolic approach to processing. In other words, it relied on processing information with symbols and structures, as well as rules to manipulate them. It wasn't until the 1980s when the field of AI took a different approach—connectionism. The most promising modeling technique of connectionism is neural networks; however, they are often met with two heavy criticisms:

  • Neural networks accept inputs of a fixed size only, which won't be of much help in real life where inputs are of variable length.
  • Neural networks are unable to bind values to specific locations within data structures that are heavily employed by the two information systems we know of—the human brain and computers. In simpler terms, in neural networks, we can't set specific weights into...
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