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Title Page
Copyright and Credits
Hands-On Meta Learning with Python
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
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
Conventions used
Get in touch
Reviews
Introduction to Meta Learning
Meta learning
Meta learning and few-shot
Types of meta learning
Learning the metric space
Learning the initializations
Learning the optimizer
Learning to learn gradient descent by gradient descent
Optimization as a model for few-shot learning
Summary
Questions
Further reading
Face and Audio Recognition Using Siamese Networks
What are siamese networks?
Architecture of siamese networks
Applications of siamese networks
Face recognition using siamese networks
Building an audio recognition model using siamese networks
Summary
Questions
Further readings
Prototypical Networks and Their Variants
Prototypical networks
Algorithm
Performing classification using prototypical networks
Gaussian prototypical network
Algorithm
Semi-prototypical networks
Summary
Questions
Further reading
Relation and Matching Networks Using TensorFlow
Relation networks
Relation networks in one-shot learning
Relation networks in few-shot learning
Relation networks in zero-shot learning
Loss function
Building relation networks using TensorFlow
Matching networks
Embedding functions
The support set embedding function (g)
The query set embedding function (f)
The architecture of matching networks
Matching networks in TensorFlow
Summary
Questions
Further reading
Memory-Augmented Neural Networks
NTM
Reading and writing in NTM
Read operation
Write operation
Erase operation
Add operation
Addressing mechanisms
Content-based addressing
Location-based addressing
Interpolation
Convolution shift
Sharpening
Copy tasks using NTM
Memory-augmented neural networks (MANN)
Read and write operations
Read operation
Write operation
Summary
Questions
Further reading
MAML and Its Variants
MAML
MAML algorithm
MAML in supervised learning
Building MAML from scratch
Generate data points
Single layer neural network
Training using MAML
MAML in reinforcement learning
Adversarial meta learning
FGSM
ADML
Building ADML from scratch
Generating data points
FGSM
Single layer neural network
Adversarial meta learning
CAML
CAML algorithm
Summary
Questions
Further reading
Meta-SGD and Reptile
Meta-SGD
Meta-SGD for supervised learning
Building Meta-SGD from scratch
Generating data points
Single layer neural network
Meta-SGD
Meta-SGD for reinforcement learning
Reptile
The Reptile algorithm
Sine wave regression using Reptile
Generating data points
Two-layered neural network
Reptile
Summary
Questions
Further readings
Gradient Agreement as an Optimization Objective
Gradient agreement as an optimization
Weight calculation
Algorithm
Building gradient agreement algorithm with MAML
Generating data points
Single layer neural network
Gradient agreement in MAML
Summary
Questions
Further reading
Recent Advancements and Next Steps
Task agnostic meta learning (TAML)
Entropy maximization/reduction
Algorithm
Inequality minimization
Inequality measures
Gini coefficient
Theil index
Variance of algorithms
Algorithm
Meta imitation learning
MIL algorithm
CACTUs
Task generation using CACTUs
Learning to learn in concept space
Key components
Concept generator
Concept discriminator
Meta learner
Loss function
Concept discrimination loss
Meta learning loss
Algorithm
Summary
Questions
Further reading
Assessments
Chapter 1: Introduction to Meta Learning
Chapter 2: Face and Audio Recognition Using Siamese Networks
Chapter 3: Prototypical Networks and Their Variants
Chapter 4: Relation and Matching Networks Using TensorFlow
Chapter 5: Memory-Augmented Neural Networks
Chapter 6: MAML and Its Variants
Chapter 7: Meta-SGD and Reptile Algorithms
Chapter 8: Gradient Agreement as an Optimization Objective
Chapter 9: Recent Advancements and Next Steps
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