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Designing Machine Learning Systems with Python 无水印pdf

Designing

Machine

Learning

Systems

Python

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Designing Machine Learning

Systems with Python

Design efcient machine learning systems that

give you more accurate results

David Julian

BIRMINGHAM - MUMBAI

Designing Machine Learning Systems with Python

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, and its dealers and distributors will be held liable for any damages

caused or alleged to be 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.

First published: April 2016

Production reference: 1310316

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78588-295-1

www.packtpub.com

Credits

Author

David Julian

Reviewer

Dr. Vahid Mirjalili

Commissioning Editor

Veena Pagare

Acquisition Editor

Tushar Gupta

Content Development Editor

Merint Thomas Mathew

Technical Editor

Abhishek R. Kotian

Copy Editor

Angad Singh

Project Coordinator

Suzanne Coutinho

Proofreader

Sas Editing

Indexer

Rekha Nair

Graphics

Disha Haria

Jason Monteiro

Production Coordinator

Aparna Bhagat

Cover Work

Aparna Bhagat

About the Author

David Julian is currently working on a machine learning project with Urban

Ecological Systems Ltd and Blue Smart Farms (http://www.bluesmartfarms.

com.au) to detect and predict insect infestation in greenhouse crops. He is currently

collecting a labeled training set that includes images and environmental data

(temperature, humidity, soil moisture, and pH), linking this data to observations of

infestation (the target variable), and using it to train neural net models. The aim is to

create a model that will reduce the need for direct observation, be able to anticipate

insect outbreaks, and subsequently control conditions. There is a brief outline of

the project at http://davejulian.net/projects/ues. David also works as a data

analyst, I.T. consultant, and trainer.

I would like to thank Hogan Gleeson, James Fuller, Kali McLaughlin

and Nadine Miller. This book would not have been possible without

the great work of the open source machine learning community.

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