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Face and Audio Recognition Using Siamese Networks

In the last chapter, we learned about what meta learning is and different types of meta learning techniques. We also saw how to learn gradient descent by gradient descent and optimization as a model for few-shot learning. In this chapter, we will learn one of the most commonly used metric-based one-shot learning algorithms called siamese networks. We will see how siamese networks learn from very few data points and how they are used to solve the low data problem. After that we will explore the architecture of siamese networks in detail and we will see some of the applications of siamese networks. At the end of this chapter, we will learn how to build face and audio recognition models using siamese networks.

In this chapter, you will learn the following:

What are siamese networks?
Architecture of siamese networks
Applications of...

Key benefits

Understand the foundations of meta learning algorithms

Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow

Master state of the art meta learning algorithms like MAML, reptile, meta SGD

Description

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.

What you will learn

Understand the basics of meta learning methods, algorithms, and types

Build voice and face recognition models using a siamese network

Learn the prototypical network along with its variants

Build relation networks and matching networks from scratch

Implement MAML and Reptile algorithms from scratch in Python

Work through imitation learning and adversarial meta learning

Explore task agnostic meta learning and deep meta learning

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Hands-On Machine Learning for Algorithmic Trading

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Anit Gupta Nov 30, 2019

The book is very nice and the explanation is pretty clear and neat. I loved the model agnostic meta learning chapter as the author has broken down such complex topic into very simple explanation.One of the advantages of this book is it covers from the few short learning algorithms to the state of the art MAML algorithms.

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Vishnu Jan 17, 2020

I stumbled upon this book's GitHub repo and bought this book. It's very well written and the algorithms are explained in the simplest way I could ever think of.Must Read for anyone willing to learn Meta-Learning!!!.

龙猫 Nov 25, 2021

Some of the theory is fairly good and clear for this book, but the code is an awful mess. There are not clear reasons why the author selected examples for certain examples. The code is also a mix of Python 2 and Python 3, so you need to have both installed. You could convert the code to Python 3, but that could be a lot of work and error prone. One problem with the text of the book is that the author used images for some of the symbols, and they don't render well for the kindle version. It is really hard to read them. I have tried reading on pc and on my phone, but some of the symbols are just impossible to read. There are some small problems with writing style, but those can be overlooked.

Vishal Jun 15, 2020

I do not recommend this book for serious readers. Most of his code comes from other's repositories on GitHub.

Saee Razi Nov 27, 2019

The code base does not work when you run it on your own machine, azure notebooks, or on google colab. When you look up errors you see that the writer copied and pasted code from the internet. This book is a ripoff. The writer has no clue what he is doing.

Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

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Hands-On Meta Learning with Python

Face and Audio Recognition Using Siamese Networks

What are siamese networks?

Face recognition using siamese networks

Building an audio recognition model using siamese networks

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