Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

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 (17 chapters)
Title Page
About Packt

Copy tasks using NTM

Now we will see how to perform a copy task using NTM. The goal of the copy task is to see how NTM stores and recalls a sequence of arbitrary length. We will feed the network a random sequence, along with a marker indicating the end of a sequence. It has to learn to output the given input sequence. So, the network will store the input sequence in the memory and then it will read back from the memory. Now, we will see step by step how to perform a copy task, and then we will see the final code as a whole at the end.

You can also check the code available as a Jupyter Notebook with an explanation here:

First, we will see how to implement the NTM cell. Instead of looking at the whole code, we will look at it line by line.

We define the NTMCellclass, where we implement the whole NTM cell:

class NTMCell():

First, we define the...