- MAML is one of the recently introduced and most commonly used meta learning algorithms, and it has lead to a major breakthrough in meta learning research. The basic idea of MAML is to find better initial parameters so that, with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.
- MAML is model agnostic, meaning that we can apply MAML for any models that are trainable with gradient descent.
- ADML is a variant of MAML that makes use of both clean and adversarial samples to find the better and robust initial model parameter, θ.
- In FGSM, we get the adversarial sample of our image and we calculate the gradients of our loss with respect to our image, more clearly input pixels of our image instead of the model parameter.
- The context parameter is a task-specific parameter that's updated on the inner loop. It is denoted by ∅ and it is specific to each task and represents the embeddings of an individual task.
- The shared parameter is shared across tasks and updated in the outer loop to find the optimal model parameter. It is denoted by θ.
Hands-On Meta Learning with Python
By :
Hands-On Meta Learning with Python
By:
Overview of this book
Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.
Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.
By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
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Current Title:
Hands-On Meta Learning with Python
Table of Contents (17 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
Free Chapter
Introduction to Meta Learning
Face and Audio Recognition Using Siamese Networks
Prototypical Networks and Their Variants
Relation and Matching Networks Using TensorFlow
Memory-Augmented Neural Networks
MAML and Its Variants
Meta-SGD and Reptile
Gradient Agreement as an Optimization Objective
Recent Advancements and Next Steps
Assessments
Other Books You May Enjoy
Index
Customer Reviews