Thimira Amaratunga - Deep Learning on Windows: Building Deep Learning Computer Vision Systems on Microsoft Windows

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6430-0 . For more detailed information, please visit http://www.apress.com/source-code .

Do you wish to learn to build practical deep learning and computer vision Systems, but are reluctant to switch to Linux for the development? Do you feel like you are more familiar with Windows, and wish that you could build everything on Windows? Well, you do not need to worry anymore. The latest deep learning and computer vision libraries have matured to the point that almost everything now can be made to work seamlessly on Windows as well. This book will show you how.

Windows OS accounts for over 70% of desktop PC usage. Windows provides many conveniences, with a wide variety of available productivity tools, causing it to gather a large user base. Furthermore, due to the better hardware compatibility and driver support, most decently to high-powered personal PCs tend to run Windows. This means that there is a large percentage of AI enthusiasts and developers out there who would like to jump into learning the remarkable capabilities of deep learning / AI. But they are reluctant or afraid to take the first steps because of the fear of the complexity of the tools and a widely held belief that AI systems can only be built on developer-friendly operating systems such as Linux. This book aims to help them move past those mental blocks and start building practical deep learning systems.

Deep learning on Windows will help you learn to build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar elements of Microsoft Windows. The goal of this book is to get as many of you interested in the field of deep learning and have the OS you build upon a nonbarrier to begin learning.

Along the way, we will learn what deep learning is and how it came to be. We will clarify some misconceptions and confusion surrounding deep learning and look at some of the major milestones it has achieved throughout the years. We will dive into coding, while learning how to apply the concepts as you build. You will learn how to set up all the tools and technologies you will need to start coding deep learning systems on a Windows system.

The book is meant for you if you are an enthusiast of machine learning and AIfrom the beginner to intermediate leveland would like to get a taste of what deep learning can do. It is meant for you if you prefer to jump in and learn through a hands-on, practical way by trying out coding and are not afraid to get your hands dirty with code. And finally, this book is meant for you if you desire to build practical, real-world AI systems.

Like many others, this book also started as a single thought. Even with experience in publishing two earlier books, it was a challenging journey. From the beginning and along the way, I have received support and encouragement from many, and I would like to express my sincere gratitude here.

First, I would like to thank the team at Apress, especially Smriti Srivastava, the acquisitions editor; Shrikant Vishwakarma, the coordinating editor; Matthew Moodie, the developmental editor; and everyone else involved in the publishing of this book.

I would also like to thank Sarani Mendis, the technical reviewer, for the excellent feedback and suggestions that added immense value to this book.

To my loving wife, Pramitha: Thank you for the encouragement and the motivation you provided from the inception of the idea to the completion. Without your support throughout the long hours and days spent writing and perfecting this book, completing it might not have been possible.