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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Engaging projects that will teach you how complex data can be exploited to gain the most insight
About This Book
- Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production.
- This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow
- It is a practical and methodically explained guide that allows you to apply Tensorflow's features from the very beginning.
Who This Book Is For
This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected.
What You Will Learn
- Load, interact, dissect, process, and save complex datasets
- Solve classification and regression problems using state of the art techniques
- Predict the outcome of a simple time series using Linear Regression modeling
- Use a Logistic Regression scheme to predict the future result of a time series
- Classify images using deep neural network schemes
- Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
- Resolve character recognition problems using the Recurrent Neural Network (RNN) model
In Detail
This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production.
Style and approach
This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine learning and numerical computation – a must-have for your bookshelf!
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Seitenzahl: 202
Veröffentlichungsjahr: 2016
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Table of Contents
Building Machine Learning Projects with TensorFlow
Building Machine Learning Projects with TensorFlow
Copyright © 2016 Packt Publishing
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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.
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First published: November 2016
Production reference: 2220317
Published by Packt Publishing Ltd.
Livery Place
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Birmingham
B3 2PB, UK.
ISBN 978-1-78646-658-7
www.packtpub.com
Credits
Author
Rodolfo Bonnin
Copy Editor
Safis Editing
Reviewer
Niko Gamulin
Project Coordinator
Nidhi Joshi
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Namrata Patil
Indexer
Mariammal Chettiyar
Content Development Editor
Siddhesh Salvi
Graphics
Disha Haria
Technical Editor
Danish Shaikh
Dharmendra Yadav
Production Coordinator
Arvindkumar Gupta
About the Author
Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.
He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008,writing a CPU and GPU - supporting neural network feed forward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.
To my wife and kids and the patience they demonstrated during the writing of this book. Also to the reviewers, who helped give professionalism to this work, and Marcos Boaglio for facilitating equipment to cover the installation chapter. Ad Maiorem Dei Gloriam.
About the Reviewer
Niko Gamulin is a senior software engineer at CloudMondo, a US-based startup, where he develops and implements predictive behavior models for humans and systems. Previously he has developed deep learning models to solve various challenges. He received his PhD in Electrical Engineering from University of Ljubljana in 2015. His research focused on creation of machine learning models for churn prediction.
I would like to thank my wonderful daughter Agata, who inspires me to gain more understanding about the learning process and Ana for being the best wife in the world.
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Preface
In recent years, machine learning has changed from a niche technology asset for scientific and theoretical experts to a ubiquitous theme in the day-to-day operations of the majority of the big players in the IT field.
This phenomenon started with the explosion in the volume of available data: During the second half of the 2000s, the advent of many kinds of cheap data capture devices (cellphones with integrated GPS, multi-megapixel cameras, and gravity sensors), and the popularization of new high-dimensional data capture (3D LIDAR and optic systems, the explosion of IOT devices, etc), made it possible to have access to a volume of information never seen before.
Additionally, in the hardware field, the almost visible limits of the Moore law, prompted the development of massive parallel devices, which multiplied the data to be used to train a determined models.
Both advancements in hardware and data availability allowed researchers to apply themselves to revisit the works of pioneers on human vision-based neural network architectures (convolutional neural networks, among others), finding many new problems in which to apply them, thanks to the general availability of data and computation capabilities.
To solve these new kinds of problems, a new interest in creating state-of-the-art machine learning packages was born, with players such as: Keras, Scikyt-learn, Theano, Caffe, and Torch, each one with a particular vision of the way machine learning models should be defined, trained, and executed.
On 9 November 2015, Google entered into the public machine learning arena, deciding to open-source its own machine learning framework, TensorFlow, on which many internal projects were based. This first 0.5 release had a numbers of shortcomings in comparison with others, a number of which were addressed later, specially the possibility of running distributed models.
So this little story brings us to this day, where TensorFlow is one of the main contenders for interested developers, as the number of projects using it as a base increases, improving its importance for the toolbox of any data science practitioner.
In this book, we will implement a wide variety of models using the TensorFlow library, aiming at having a low barrier of entrance and providing a detailed approach to the problem solutions.
What this book covers
Chapter 1, Exploring and Transforming Data, guides the reader in undersanding the main components of a TensorFlow application, and the main data-exploring methods included.
Chapter 2, Clustering, tells you about the possibility of grouping different kinds of data elements, defining a previous similarity criteria.
Chapter 3, Linear Regression, allows the reader to define the first mathematical model to explain diverse phenomena.
Chapter 4, Logistic Regression, is the first step in modeling non-linear phenomena with a very powerful and simple mathematical function.
Chapter 5, Simple Feedforward Neural Networks, allows you to comprehend the main component, and mechanisms of neural networks.
Chapter 6, Convolutional Neural Networks, explains the functioning and practical application, of this recently rediscovered set of special networks.
Chapter 7, Recurrent Neural Networks, shows a detailed explanation of this very useful architecture for temporal series of data.
Chapter 8, Deep Neural Networks, offers an overview of the latest developments on mixed layer type neural networks.
Chapter 9, Running Models at Scale – GPU and Serving, explains the ways of tackling problems of greater complexity, by dividing the work into coordinating units.
Chapter 10, Library Installation and Additional Tips, covers the installation of TensorFlow on Linux, Windows, and Mac architectures, and presents you with some useful code tricks that will ease day-to-day tasks.
What you need for this book
Software required (with version)
Hardware specifications
OS required
TensorFlow 0.10, Jupyter Notebook
Any x86 computer
Ubuntu Linux 16.04
Who this book is for
This book is for data analysts, data scientists, and researchers who want to make the results of their machine learning activities faster and more efficient. Those who want a crisp guide to complex numerical computations with TensorFlow will find the book extremely helpful. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected.
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