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Index
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
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