Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Azure Data and AI Architect Handbook. Adopt a structured approach to designing data and AI solutions at scale on Microsoft Azure

Olivier Mertens, Breght Van Baelen

With data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution.You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions.By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.

Hands-On One-shot Learning with Python. Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

Shruti Jadon, Ankush Garg

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.

IBM DB2 9.7 Advanced Application Developer Cookbook. This cookbook is essential reading for every ambitious IBM DB2 application developer. With over 70 practical recipes, it will help you master the most sophisticated elements and techniques used in designing high quality DB2 applications

Sanjay Kumar, Mohankumar Saraswatipura

With lots of new features, DB2 9.7 delivers one the best relational database systems in the market. DB2 pureXML optimizes Web 2.0 and SOA applications.DB2 LUW database software offers industry leading performance, scale, and reliability on your choice of platform on various Linux distributions, leading Unix Systems like AIX, HP-UX and Solaris and MS Windows platforms. This DB2 9.7 Advanced Application Developer Cookbook will provide an in-depth quick reference during any application's design and development. This practical cookbook focuses on advanced application development areas that include performance tips and the most useful DB2 features that help in designing high quality applications. This book dives deep into tips and tricks for optimized application performance.With this book you will learn how to use various DB2 features in database applications in an interactive way.

LaTeX Graphics with TikZ. A practitioner's guide to drawing 2D and 3D images, diagrams, charts, and plots

Stefan Kottwitz

In this first-of-its-kind TikZ book, you’ll embark on a journey to discover the fascinating realm of TikZ—what it’s about, the philosophy behind it, and what sets it apart from other graphics libraries. From installation procedures to the intricacies of its syntax, this comprehensive guide will help you use TikZ to create flawless graphics to captivate your audience in theses, articles, or books.You’ll learn all the details starting with drawing nodes, edges, and arrows and arranging them with perfect alignment. As you explore advanced features, you’ll gain proficiency in using colors and transparency for filling and shading, and clipping image parts. You’ll learn to define TikZ styles and work with coordinate calculations and transformations.That’s not all! You’ll work with layers, overlays, absolute positioning, and adding special decorations and take it a step further using add-on packages for drawing diagrams, charts, and plots.By the end of this TikZ book, you’ll have mastered the finer details of image creation, enabling you to achieve visually stunning graphics with great precision.

Android Application Programming with OpenCV. For Java developers OpenCV is a fantastic opportunity to benefit from the popularity of image related mobile apps on Android. This book teaches you all you need to know about computer vision with practical projects

Joseph Howse

Take a smartphone from your pocket, and within a few seconds, you can snap a photo, manipulate it, and share it with the world. You have just achieved mass production of image data. With a computer vision library such as OpenCV, you can analyze and transform copious amounts of image data in real time on a mobile device. The upshot to this is that you, as developers, can provide mobile users with many new kinds of images, constantly highlighting certain visual features that are of artistic or practical interest. Android is a convenient platform for such experiments because it uses a high-level language (Java), it provides standardized interfaces for sharing image data between applications, and it is mostly open source, so everyone can study its implementation.Android Application Programming with OpenCV is a practical, hands-on guide that covers the fundamental tasks of computer vision—capturing, filtering, and analyzing images-with step-by-step instructions for writing both an application and reusable library classes.Android Application Programming with OpenCV looks at OpenCV's Java bindings for Android and dispels mysteries such as which version of these bindings to use, how to integrate with standard Android functionality for layout, event handling, and data sharing, and how to integrate with OpenGL for rendering. By following the clear, concise, and modular examples provided in this book, you will develop an application that previews, captures, and shares photos with special effects based on color manipulation, edge detection, image tracking, and 3D rendering.Beneath the application layer, you will develop a small but extensible library that you can reuse in your future projects. This library will include filters for selectively modifying an image based on edge detection, 2D and 3D image trackers, and adapters to convert the Android system's camera specifications into OpenCV and OpenGL projection matrices. If you want a quick start in computer vision for Android, then this is the book for you.By the end of Android Application Programming with OpenCV, you will have developed a computer vision application that integrates OpenCV, Android SDK, and OpenGL.

Git Best Practices Guide. Master the best practices of Git with the help of real-time scenarios to maximize team efficiency and workflow

PIDOUX Eric

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.