Ahmed Menshawy - Deep Learning By Example
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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.
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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: Vedika Naik
Acquisition Editor: Tushar Gupta
Content Development Editor: Aishwarya Pandere
Technical Editor: Sagar Sawant
Copy Editor: Vikrant Phadke, Safis Editing
Project Coordinator: Nidhi Joshi
Proofreader: Safis Editing
Indexer: Mariammal Chettiyar
Graphics: Tania Dutta
Production Coordinator: Aparna Bhagat
First published: February 2018
Production reference: 1260218
Published by Packt Publishing Ltd.
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ISBN 978-1-78839-990-6
www.packtpub.com
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Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.
Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a Researcher at the Insight Centre for Data Analytics, Ireland. Earlier, he worked as a Lead Engineer at Samsung Electronics, Korea.
He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published research papers concerning bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, DeepLearning4j, MXNet, and H2O.
Doug Ortiz is an experienced Enterprise Cloud, Big Data, Data Analytics and Solutions Architect who has architected, designed, developed, re-engineered and integrated enterprise solutions. Other expertise: Amazon Web Services, Azure, Google Cloud, Business Intelligence, Hadoop, Spark, NoSQL Databases, SharePoint to mention a few.
Is the founder of Illustris, LLC reachable at: dougortiz@illustris.org
Huge thanks to my wonderful wife Milla, Maria, Nikolay and our children for all their support.
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This book will start off by introducing the foundations of machine learning, what makes learning visible, demonstrating the traditional machine learning techniques with some examples and eventually deep learning. You will then move to creating machine learning models that will eventually lead you to neural networks. You will get familiar with the basics of deep learning and explore various tools that enable deep learning in a powerful yet user-friendly manner. With a very low starting point, this book will enable a regular developer to get hands-on experience with deep learning. You will learn all the essentials needed to explore and understand what deep learning is and will perform deep learning tasks first-hand. Also, we will be using one of the most widely used deep learning frameworks. TensorFlow has big community support that is growing day by day, which makes it a good option for building your complex deep learning applications.
This book is a starting point for those who are keen on knowing about deep learning and implementing it but do not have an extensive background in machine learning, complex statistics, and linear algebra.
, Data science - Bird's-eye view , explains that data science or machine learning is the process of giving the machines the ability to learn from a dataset without being told or programmed. For instance, it will be extremely hard to write a program that takes a hand-written digit as an input image and outputs a value from 0-9 according to the number that's written in this image. The same applies to the task of classifying incoming emails as spam or non-spam. To solve such tasks, data scientists uses learning methods and tools from the field of data science or machine learning to teach the computer how to automatically recognize digits by giving it some explanatory features that can distinguish each digit from another. The same for the spam/non-spam problem, instead of using regular expressions and writing hundred of rules to classify the incoming emails, we can teach the computer through specific learning algorithms how to distinguish between spam and non-spam emails.
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