- When the gradients of all tasks are in the same direction, then it is called gradient agreement, and when the gradient of some tasks differ greatly from others, then it is called gradient disagreement.
- The update equation in gradient agreement can be expressed as .
- Weights are proportional to the inner product of the gradients of a task and the average of gradients of all of the tasks in the sampled batch of tasks.
The weights are calculated as follows:
- The normalization factor is proportional to the inner product of and
.
- If the gradient of a task is in the same direction as the average gradient of all tasks in a sampled batch of tasks, then we can increase its weights so that it'll contribute more when updating our model parameter. Similarly, if the gradient of a task is in the direction that's greatly different from the average gradient of all tasks in a sampled batch of tasks, then we can decrease its weights so that it'll contribute less when updating our model parameter.
Hands-On Meta Learning with Python
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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