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

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LearnUnderstand the basics of meta learning methods, algorithms, and typesBuild voice and face recognition models using a siamese networkLearn the prototypical network along with its variantsBuild relation networks and matching networks from scratchImplement MAML and Reptile algorithms from scratch in PythonWork through imitation learning and adversarial meta learningExplore task agnostic meta learning and deep meta learningAboutMeta 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.FeaturesUnderstand the foundations of meta learning algorithmsExplore practical examples to explore various one-shot learning algorithms with its applications in TensorFlowMaster state of the art meta learning algorithms like MAML, reptile, meta SGD

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

BIRMINGHAM - MUMBAI Hands-On Meta Learning with Python Copyright 2018 Packt - photo 2

BIRMINGHAM - MUMBAI
Hands-On Meta Learning with Python

Copyright 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Pavan Ramchandani
Acquisition Editor: Pavan Ramchandani
Content Development Editor: Chris D'cruz
Technical Editor: Dinesh Pawar
Copy Editor: Safis Editing
Project Coordinator: Namrata Swetta
Proofreader: Safis Editing
Indexer: Tejal Daruwale Soni
Graphics: Tom Scaria
Production Coordinator: Nilesh Mohite

First published: December 2018

Production reference: 1261218

Published by Packt Publishing Ltd.
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B3 2PB, UK.

ISBN 978-1-78953-420-7

www.packtpub.com


To my adorable Mom, Kasthury, and to my beloved Dad, Ravichandiran.
- Sudharsan Ravichandiran
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Contributors
About the author

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning ). He completed his bachelor's in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.

I would like to thank my amazing parents and my brother, Karthikeyan, for inspiring and motivating me. My big thanks to my best friend, Nikhil Aditya. Huge thanks to Veena Pagare for giving me an amazing opportunity. Special thanks to my dear friend, Sri Hari Charan, who shaped my life. I can't thank enough my best friend, Gautham, for cheering me up through all my tough times, and I am forever grateful to My Soeor, who always has my back.
What this book covers

, Introduction to Meta Learning, helps us to understand what meta learning is and covers the different types of meta learning. We will also learn how meta learning uses few-shot learning by learning from a few data points. We will then see how to become familiar with gradient descent. Later in the chapter, we will see optimization as a model for the few shot learning setting.
, Face and Audio Recognition Using Siamese Networks, starts by explaining what siamese networks are and how siamese networks are used in the one-shot learning setting. We will look at the architecture of a siamese network and some of the applications of a siamese network. Then, we will see how to use the siamese networks to build face and audio recognition models.
, Prototypical Networks and Their Variants, explains what prototypical networks are and how they are used in the few shot learning scenario. We will see how to build a prototypical network to perform classification on an omniglot character set. Later in the chapter, we will look at different variants of prototypical networks, such as the Gaussian prototypical networks and semi-prototypical networks.

, Relation and Matching Networks Using TensorFlow, helps us to understand the relation network architecture and how relation network is used in one-shot, few-shot, and zero-shot learning settings. We will then see how to build a relation network using TensorFlow. Next, we will learn about the matching network and its architecture. We will also explore full contextual embeddings and how to build a matching network using TensorFlow.
, Memory-Augmented Neural Networks, covers what neural Turing machines (NTMs) are and how they make use of external memory for storing and retrieving information. We will look at different addressing mechanisms used in NTMs and then we will learn about memory augmented neural networks and how they differ from the NTM architecture.
, MAML and Its Variants, deals with one of the popular meta learning algorithms, called model-agnostic meta learning (MAML). We will explore what MAML is and how it is used in supervised and reinforcement learning settings. We will also see how to build MAML from scratch. Then, we will learn about adversarial meta learning and CAML, which is used for fast context adaptation in meta learning.
, Meta-SGD and Reptile, explain how meta-SGD is used to learn all the ingredients of gradient descent algorithms, such as initial weights, learning rates, and the update direction. We will see how to build meta-SGD from scratch. Later in the chapter, we will learn about the reptile algorithm and see how it serves as an improvement over MAML. We will also see how to use the reptile algorithm for sine wave regression.

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