- NTM is an interesting algorithm that has the ability to store and retrieve information from memory. The idea of NTM is to augment the neural network with external memory—that is, instead of using hidden states as memory, it uses external memory to store and retrieve information.
- The controller is basically a feed-forward neural network or recurrent neural network. It reads from and writes to memory.
- The read head and write head are the pointers containing addresses of the memory that it has to read from and write to.
- The memory matrix or memory bank, or simply the memory, is where we will store the information. Memory is basically a two-dimensional matrix composed of memory cells. The memory matrix contains N rows and M columns. Using the controller, we access the content from the memory...
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
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.
Table of Contents (12 chapters)
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
Customer Reviews
