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

Building gradient agreement algorithm with MAML

In the last section, we saw how the gradient agreement algorithm works. We saw how gradient agreement adds weights to the gradients implying their importance. Now, we'll see how to use our gradient agreement algorithm with MAML by coding them from scratch using NumPy. For better understanding, we'll consider a simple binary classification task. We'll randomly generate our input data, train it with a simple single-layer neural network, and try to find the optimal parameter θ.

Now we'll see step by step exactly how to do this.

You can also check out the whole code, available as a Jupyter Notebook here:

We import all of the necessary libraries:

import numpy as np

Generating data points

Now, we define a function called sample_points for generating...