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The Python Workshop: A Practical, No-Nonsense Introduction to Python Development
by Andrew Bird Graham Lee Dr Lau Han Mario Corchero Jimenez Corey WadeCut through the noise and get real results with a step-by-step approach to learning Python 3.X programming Key Features Experimental projects showcasing the implementation of high-performance deep learning models with Keras. Use-cases across reinforcement learning, natural language processing, GANs and computer vision. Build strong fundamentals of Keras in the area of deep learning and artificial intelligence Book Description You already know you want to learn Python, and a smarter way to learn Python 3 is to learn by doing. The Python Workshop focuses on building up your practical skills so that you can work towards building up your machine learning skills as a data scientist, write scripts that help automate your life and save you time, or even create your own games and desktop applications. You'll learn from real examples that lead to real results. Throughout The Python Workshop, you'll take an engaging step-by-step approach to understanding Python. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning about Python scripting. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical copy of The Python Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive free content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your Python book. Fast-paced and direct, The Python Workshop is the ideal companion for Python beginners. You'll build and iterate on your code like a software developer, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Learn how to write clean and concise code with Python 3 Understand classes and object-oriented programming Tackle entry-level data science and create engaging visualizations Use Python to create responsive, modern web applications Automate essential day-to-day tasks with Python scripts Get started with predictive Python machine learning Who this book is for This book is designed for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner's book, it will help if you already know standard programming topics, such as variables, if-else statements, and functions. Experience with another object-oriented program is beneficial, but not mandatory.
The Python Workshop: Write Python code to solve challenging real-world problems, 2nd Edition
by Corey Wade Mario Corchero Jimenez Andrew Bird Dr. Lau Han Graham LeeGain proficiency, productivity, and power by working on projects and kick-starting your career in Python with this comprehensive, hands-on guide.Key FeaturesUnderstand and utilize Python syntax, objects, methods, and best practicesExplore Python's many features and libraries through real-world problems and big dataUse your newly acquired Python skills in machine learning as well as web and software developmentBook DescriptionPython is among the most popular programming languages in the world. It's ideal for beginners because it's easy to read and write, and for developers, because it's widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed.This project-based course has been designed by a team of expert authors to get you up and running with Python. You'll work though engaging projects that'll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact.By completing the course from start to finish, you'll walk away feeling capable of tackling any real-world Python development problem.What you will learnWrite efficient and concise functions using core Python methods and librariesBuild classes to address different business needsCreate visual graphs to communicate key data insightsOrganize big data and use machine learning to make regression and classification predictionsDevelop web pages and programs with Python tools and packagesAutomate essential tasks using Python scripts in real-time executionWho this book is forThis book is for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner's course, you'll learn more easily if you already have an understanding of standard programming topics like variables, if-else statements, and functions. Experience with another object-oriented program, though not essential, will also be beneficial. If Python is your first attempt at computer programming, this book will help you understand the basics with adequate detail for a motivated student.
Python & XML: XML Processing with Python
by Christopher A. Jones Fred L. Drake JrIf you are a Python programmer who wants to incorporate XML into your skill set, this is the book for you. Python has attracted a wide variety of developers, who use it either as glue to connect critical programming tasks together, or as a complete cross-platform application development language. Yet, because it is object-oriented and has powerful text manipulation abilities, Python is an ideal language for manipulating XML.Python & XML gives you a solid foundation for using these two languages together. Loaded with practical examples, this new volume highlights common application tasks, so that you can learn by doing. The book starts with the basics then quickly progresses to complex topics, like transforming XML with XSLT, querying XML with XPath, and working with XML dialects and validation. It also explores the more advanced issues: using Python with SOAP and distributed web services, and using Python to create scalable streams between distributed applications (like databases and web servers).The book provides effective practical applications, while referencing many of the tools involved in XML processing and Python, and highlights cross-platform issues along with tasks relevant to enterprise computing. You will find ample coverage of XML flow analysis and details on ways in which you can transport XML through your network.Whether you are using Python as an application language, or as an administrative or middleware scripting language, you are sure to benefit from this book. If you want to use Python to manipulate XML, this is your guide.
Python & XML
by Christopher A. Jones Frederick L. Shaw Jr.Python is an ideal language for manipulating XML, and this new volume gives you a solid foundation for using these two languages together. Complete with practical examples that highlight common application tasks, the book starts with the basics then quickly progresses to complex topics, like transforming XML with XSLT and querying XML with XPath. It also explores more advanced subjects, such as SOAP and distributed web services.
A Pythonic Adventure: From Python basics to a working web app
by Pavel AnniTime to take an adventure with friends! Team up with Erik and Simon to learn Python the easy way. This colorful book uses engaging questions and lively conversations to introduce computer programming to young readers one step at a time.In A Pythonic Adventure, you will learn useful Python skills like: Installing Python Working with files Creating text-based dialogs and menus Using if/then, loops, lists, dictionaries, and input/output Building web applications Making your web apps look super professional It&’s fun to learn with friends! In A Pythonic Adventure you&’ll meet Erik and Simon, two brothers who are just beginning their Python journey. Join them as they chat about the language, learn the basics, and build some cool programs. The book&’s dialogue helps young programmers understand complex concepts much more easily. It's the perfect way for young programmers (and their parents) to get started. There&’s no boring lessons or dull exercises in this adventure. You&’ll follow Erik and Simon&’s questions and mistakes, discover how to write programs with a team, and get a chance to create applications you can use in your daily life. By the time they&’re done reading, young learners will not only know how to write code, they&’ll know how to think about problems like professional developers. All code in this book runs on Mac, Windows, Linux, and Raspberry Pi. About the technology Computer programming is an adventure, full of new experiences, challenges, triumphs, and mistakes. In A Pythonic Adventure, you&’ll join brothers Erik and Simon as they learn to create their first Python program. Written especially for young readers, this book is the perfect introduction to a skill that will last a lifetime! About the book A Pythonic Adventure teaches you to code by asking questions, making errors, and trying out different solutions—just like in real life. As you go, you&’ll create a web application for a coffee shop step-by-step, from your first online menu to saving orders in a database. And this unique tutorial goes deeper than other beginner books. You&’ll learn and practice important skills like planning applications, finding bugs, and managing user expectations. What's inside Installing Python Creating text-based dialogs and menus Building web applications Making your web apps look professional About the reader For readers aged 10+. Perfect for adult beginners, too! About the author Pavel Anni is a Principal Customer Engineer at SambaNova Systems, and has also worked for Sun Microsystems, Oracle, and Red Hat. Table of Contents 1 Coffee for friends: First steps 2 Lists: What&’s on the menu? 3 Functions: Don&’t repeat yourself! 4 User errors: Everybody makes mistakes 5 Working with files: Being a shop manager 6 Main menu: Next customer! 7 Creating functions: Get the order and print it 8 Working with JSON: Save the order 9 Complete the menu: A real program 10 Learning Flask: Your first web application 11 Web form for orders: Coffee shop on the web 12 Database: We need good storage 13 Styles: Making it pretty 14 Help from AI: Improving our code 15 Next steps: Plans for the future
Pythonic Programming
by Dmitry ZinovievMake your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break. Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented. This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers. Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips. What You Need: You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
PyTorch 1.0 Reinforcement Learning Cookbook: Over 60 Recipes To Design, Develop, And Deploy Self-learning Ai Models Using Python
by Yuxi (Hayden) LiuMachine learning engineers, data scientists and AI researchers looking for quick solutions to different problems in RL will find the book useful. Prior exposure to machine learning concepts is required, while previous experience with PyTorch will be a bonus.
PyTorch Artificial Intelligence Fundamentals: A recipe-based approach to design, build and deploy your own AI models with PyTorch 1.x
by Jibin MathewUse PyTorch to build end-to-end artificial intelligence systems using Python Key Features Build smart AI systems to handle real-world problems using PyTorch 1.x Become well-versed with concepts such as deep reinforcement learning (DRL) and genetic programming Cover PyTorch functionalities from tensor manipulation through to deploying in production Book Description Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems. This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, you'll build a medical image classifier using deep learning. Next, you'll use TensorBoard for visualizations. You'll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. You'll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. You'll learn to implement AI tasks and tackle real-world problems in computer vision, natural language processing (NLP), and other real-world domains. By the end of this book, you'll have the foundations of the most important and widely used techniques in AI using the PyTorch framework. What you will learn Perform tensor manipulation using PyTorch Train a fully connected neural network Advance from simple neural networks to convolutional neural networks (CNNs) and recurrent neural networks (RNNs) Implement transfer learning techniques to classify medical images Get to grips with generative adversarial networks (GANs), along with their implementation Build deep reinforcement learning applications and learn how agents interact in the real environment Scale models to production using ONNX Runtime Deploy AI models and perform distributed training on large datasets Who this book is for This PyTorch book is for AI engineers who are just getting started, machine learning engineers, data scientists and deep learning enthusiasts who are looking for a guide to help them solve AI problems effectively. Working knowledge of the Python programming language and a basic understanding of machine learning are expected.
PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x
by Michael AvendiDiscover powerful ways to use deep learning algorithms and solve real-world computer vision problems using Python Key Features Solve the trickiest of problems in computer vision by combining the power of deep learning and neural networks Leverage PyTorch 1.x capabilities to perform image classification, object detection, and more Train and deploy enterprise-grade, deep learning models for computer vision applications Book Description Computer vision techniques play an integral role in helping developers gain a high-level understanding of digital images and videos. With this book, you'll learn how to solve the trickiest problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of CV tasks. Starting with a quick overview of the PyTorch library and key deep learning concepts, the book then covers common and not-so-common challenges faced while performing image recognition, image segmentation, object detection, image generation, and other tasks. Next, you'll understand how to implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you'll learn how to solve any issue you might face while fine-tuning the performance of a model or integrating it into your application. Later, you'll get to grips with scaling your model to handle larger workloads, and implementing best practices for training models efficiently. By the end of this CV book, you'll be proficient in confidently solving many CV related problems using deep learning and PyTorch. What you will learn Develop, train and deploy deep learning algorithms using PyTorch 1.x Understand how to fine-tune and change hyperparameters to train deep learning algorithms Perform various CV tasks such as classification, detection, and segmentation Implement a neural style transfer network based on CNNs and pre-trained models Generate new images and implement adversarial attacks using GANs Implement video classification models based on RNN, LSTM, and 3D-CNN Discover best practices for training and deploying deep learning algorithms for CV applications Who this book is for Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate-level knowledge of computer vision concepts, along with Python programming experience is required.
PyTorch Deep Learning Hands-On: Apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more
by Sherin Thomas Sudhanshu PassiAll the key deep learning methods built step-by-step in PyTorchKey FeaturesUnderstand the internals and principles of PyTorchImplement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook DescriptionPyTorch is a new, lightweight, and Python-first tool for deep learning. Built by Facebook to offer flexibility and speed, it has quickly become the preferred tool for deep learning experts. PyTorch helps you release deep learning models faster than ever before.PyTorch Deep Learning Hands-On shows how to implement every major deep learning architecture in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.Each chapter focuses on a different area of deep learning. Chapters start with a refresher on the core principles, before sharing the code you need to implement them in PyTorch.If you want to become a deep learning expert this book is for you.What you will learnUse PyTorch to build:Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and moreConvolutional Neural Networks – create advanced computer vision systemsRecurrent Neural Networks – work with sequential data such as natural language and audioGenerative Adversarial Networks – create new content with models including SimpleGAN and CycleGANReinforcement Learning – develop systems that can solve complex problems such as driving or game playingDeep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packagesProduction-ready models – package your models for high-performance production environmentsWho this book is forMachine learning professionals and enthusiasts who know Python and want to build efficient and powerful deep learning systems in PyTorch.
PyTorch Pocket Reference
by Joe PapaThis concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.Learn basic PyTorch syntax and design patternsCreate custom models and data transformsTrain and deploy models using a GPU and TPUTrain and test a deep learning classifierAccelerate training using optimization and distributed trainingAccess useful PyTorch libraries and the PyTorch ecosystem
PyTorch Recipes: A Problem-Solution Approach
by Pradeepta MishraGet up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch.What You Will LearnMaster tensor operations for dynamic graph-based calculations using PyTorchCreate PyTorch transformations and graph computations for neural networksCarry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNNBuild LSTM models in PyTorch Use PyTorch for text processing Who This Book Is ForReaders wanting to dive straight into programming PyTorch.
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
by Pradeepta MishraLearn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.By the end of this book, you will be able to confidently build neural network models using PyTorch.What You Will LearnUtilize new code snippets and models to train machine learning models using PyTorchTrain deep learning models with fewer and smarter implementationsExplore the PyTorch framework for model explainability and to bring transparency to model interpretationBuild, train, and deploy neural network models designed to scale with PyTorchUnderstand best practices for evaluating and fine-tuning models using PyTorchUse advanced torch features in training deep neural networksExplore various neural network models using PyTorchDiscover functions compatible with sci-kit learn compatible modelsPerform distributed PyTorch training and executionWho This Book Is ForMachine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.
Q# Pocket Guide: Instant Help for Q# Developers
by Mariia MykhailovaReady to build quantum computing applications using Q# and the Microsoft Quantum Development Kit? This is the book for you. Q# is a domain-specific language for expressing quantum algorithms that combines familiar "classical" language constructs with quantum-specific ones. Ideal for any developer familiar with (or willing to learn) the basics of quantum computing and looking to get started with quantum programming, this pocket guide quickly helps you find syntax and usage information for unfamiliar aspects of Q#.You'll explore the quantum software development lifecycle from implementing the program to running it on quantum simulators to testing and debugging it. You'll learn to use the tools provided by Microsoft's Quantum Development Kit for each step of the process.You'll explore:Q# language details, including data types, statements, and operatorsGuidelines for organizing Q# code and invoking it from different environmentsInformation on simulators and tools in the Microsoft Quantum Development KitAdvice on testing and debugging tools and techniques for quantum programs
Qbasic by Example (Special Edition)
by Greg M. Perry Que Publishing StaffThis book began in 1978, when I first turned on a computer and opened a BASIC manual to page 1, having never touched a computer before. Since then, I have continued to learn and teach BASIC. From that first Microsoft BASIC language, I taught many others, but Microsoft's variants of BASIC continue to be my favorites and old standbys. I am glad that Mr. Gates and his crew at Microsoft continue to support this language; it looks as though they are dedicated, more than ever, to keeping the BASIC flame alive with QBasic.
QGIS 2 Cookbook
by Alexander Bruy Victor Olaya Ferrero Anita Graser Alex MandelBecome a QGIS power user and master QGIS data management, visualization, and spatial analysis techniques About This Book * Explore and create time-based visualizations and build interactive maps * Maximize your use of the QGIS features, plugins and toolbox automation * Packed with lots of sample datasets to enable a better understanding of the code Who This Book Is For If you are an intermediate GIS user, with either previous experience in QGIS or any other GIS application, this is the book for you. The recipes can be used to learn more advanced techniques in QGIS or to replicate the functionalities equivalent to other GIS platforms. This book assumes that you already have a working QGIS system in place. What You Will Learn * Import and export common tricky spatial data formats * Perform classic vector and raster analysis with QGIS * Utilize spatial databases and data management tools * Use and create geographic web services and maps * Explore and create time-based visualizations * Perform network building and routing analysis * Extend QGIS capabilities with popular plugins and toolbox automation * Make beautiful and unique maps with customized cartography In Detail QGIS is a user-friendly, cross-platform desktop geographic information system used to make maps and analyze spatial data. QGIS allows users to understand, question, interpret, and visualize spatial data in many ways that reveal relationships, patterns, and trends in the form of maps. This book is a collection of simple to advanced techniques that are needed in everyday geospatial work, and shows how to accomplish them with QGIS. You will begin by understanding the different types of data management techniques, as well as how data exploration works. You will then learn how to perform classic vector and raster analysis with QGIS, apart from creating time-based visualizations. Finally, you will learn how to create interactive and visually appealing maps with custom cartography. By the end of this book, you will have all the necessary knowledge to handle spatial data management, exploration, and visualization tasks in QGIS. Style and approach This book covers practical examples, with step-by-step instructions on how to use real world data covering common GIS operations and the different analysis techniques. It provides detailed explanations and applications of QGIS concepts that will allow the user to effectively analyze spatial data.
QGIS Blueprints
by Ben MearnsDevelop analytical location-based web applications with QGIS About This Book * Tame geographic information workflows with QGIS blueprints for smart web applications * Create geographic web applications using QGIS and free/open source software * Blueprints provide real-world applications covering many use cases Who This Book Is For This book encompasses relatively experienced GIS developers who have a strong grounding in the fundamentals of GIS development. They will have used QGIS before, but are looking to understand how to develop more complex, layered map applications that expose various data sets, utilize different visualizations, and are consumable (usable) by end users What You Will Learn * Review geographic information principles and the application of these principles in the QGIS free/open source ecosystem * Perform advanced analysis with site selection, hydrologic, and topological networks * Build performant web applications by tile caching and generating static assets * Provide collaborative editing capabilities for your team or community * Develop custom and dynamic analysis and visualization capabilities * Select the best components from desktop and web, for your use case * Integrate it with social media and crowdsourcing In Detail QGIS, the world's most popular free/open source desktop geographic information system software, enables a wide variety of use cases involving location - previously only available through expensive specialized commercial software. However, designing and executing a multi-tiered project from scratch on this complex ecosystem remains a significant challenge. This book starts with a primer on QGIS and closely related data, software, and systems. We'll guide you through six use-case blueprints for geographic web applications. Each blueprint boils down a complex workflow into steps you can follow to reduce time lost to trial and error. By the end of this book readers should be able to build complex layered applications that visualize multiple data sets, employing different types of visualization, and give end users the ability to interact with and manipulate this data for the purpose of analysis. Style and approach This is a comprehensive guide to the application of QGIS and free/open source software in creating web applications from analysis. Step-by-step blueprints guide the reader through analytical and web development topics and designs.
QGIS By Example
by Alexander Bruy Daria SvidzinskaIf you are a beginner or an intermediate GIS user, this book is for you. It is ideal for practitioners, data analysts, and application developers who have very little or no familiarity with geospatial data and software.
QGIS Python Programming Cookbook
by Joel LawheadIf you are a geospatial analyst who wants to learn more about automating everyday GIS tasks or a programmer who is responsible for building GIS applications,this book is for you. The short, reusable recipes make concepts easy to understand. You can build larger applications that are easy to maintain when they are put together.
QGIS Python Programming Cookbook
by Joel LawheadMaster over 170 recipes that will help you turn QGIS from a desktop GIS tool into a powerful automated geospatial framework About This Book Delve into the undocumented features of the QGIS API Get a set of user-friendly recipes that can automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes This book has a complete code upgrade to QGIS 2.18 and 30 new, valuable recipes Who This Book Is For This book is for geospatial analysts who want to learn more about automating everyday GIS tasks as well as programmers responsible for building GIS applications. The short, reusable recipes make concepts easy to understand and combine so you can build larger applications that are easy to maintain. What You Will Learn Use Python and QGIS to produce captivating GIS visualizations and build complex map layouts Find out how to effectively use the poorly-documented and undocumented features of the QGIS Python API Automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes Create, import, and edit geospatial data on disk or in-memory Change QGIS settings programmatically to control default behavior Automatically generate PDF map books Build dynamic forms for field input In Detail QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we'll also share a few tips and tricks based on different aspects of QGIS. Style and approach This book follows a recipe-based problem-solution approach to address and dispel challenges faced when implementing and using QGIS on a regular basis.
QGIS Python Programming Cookbook - Second Edition
by Joel LawheadMaster over 170 recipes that will help you turn QGIS from a desktop GIS tool into a powerful automated geospatial framework About This Book • Delve into the undocumented features of the QGIS API • Get a set of user-friendly recipes that can automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes • This book has a complete code upgrade to QGIS 2.18 and 30 new, valuable recipes Who This Book Is For This book is for geospatial analysts who want to learn more about automating everyday GIS tasks as well as programmers responsible for building GIS applications. The short, reusable recipes make concepts easy to understand and combine so you can build larger applications that are easy to maintain. What You Will Learn • Use Python and QGIS to produce captivating GIS visualizations and build complex map layouts • Find out how to effectively use the poorly-documented and undocumented features of the QGIS Python API • Automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes • Create, import, and edit geospatial data on disk or in-memory • Change QGIS settings programmatically to control default behavior • Automatically generate PDF map books • Build dynamic forms for field input In Detail QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we'll also share a few tips and tricks based on different aspects of QGIS. Style and approach This book follows a recipe-based problem-solution approach to address and dispel challenges faced when implementing and using QGIS on a regular basis.
QGIS Quick Start Guide: A beginner's guide to getting started with QGIS 3.4
by Andrew CuttsStep through loading GIS data, creating GIS data, styling GIS and making maps with QGIS following a simple narrative that will allow you to build confidence as you progress. Key Features Work with GIS data, a step by step guide from creation to making a map Perform geoprocessing tasks and automate them using model builder Explore a range of features in QGIS 3.4, discover the power behind open source desktop GIS Book Description QGIS is a user friendly, open source geographic information system (GIS). The popularity of open source GIS and QGIS, in particular, has been growing rapidly over the last few years. This book is designed to help beginners learn about all the tools required to use QGIS 3.4. This book will provide you with clear, step-by-step instructions to help you apply your GIS knowledge to QGIS. You begin with an overview of QGIS 3.4 and its installation. You will learn how to load existing spatial data and create vector data from scratch. You will then be creating styles and labels for maps. The final two chapters demonstrate the Processing toolbox and include a brief investigation on how to extend QGIS. Throughout this book, we will be using the GeoPackage format, and we will also discuss how QGIS can support many different types of data. Finally, you will learn where to get help and how to become engaged with the GIS community. What you will learn Use existing data to interact with the canvas via zoom/pan/selection Create vector data and a GeoPackage and build a simple project around it Style data, both vector and raster data, using the Layer Styling Panel Design, label, save, and export maps using the data you have created Analyze spatial queries using the Processing toolbox Expand QGIS with the help of plugins, model builder, and the command line Who this book is for If you know the basic functions and processes of GIS, and want to learn to use QGIS to analyze geospatial data and create rich mapping applications, then this is the book for you.
Qiskit Pocket Guide: Quantum Development with Qiskit
by James L. Weaver Frank J. HarkinsThe quantum computing market is predicted to grow by nearly $1.3 billion over the next five years. Why? Given their quantum mechanical nature, quantum computers are expected to solve difficult problems in chemistry, optimization, finance, and machine learning that classical computers find impossible to unravel.This pocket guide provides software developers with a quick reference to Qiskit, an open source SDK for working with quantum computers. Packed with helpful programming examples, tables, figures, and lists, this handy book helps you find the information you need to develop and debug quantum programs.Whether you're focused on business, engineering, or scientific applications, you'll learn how to choose and apply appropriate Qiskit classes, methods, and functions.Learn how to create quantum circuits, define quantum gates, and leverage the transpilerExplore modules for implementing quantum information concepts and quantum algorithmsSurvey features of Qiskit that abstract and facilitate working with various quantum computers and simulatorsLearn how to use the latest version of the Open Quantum Assembly Language
Qlik Sense: Create smart data visualizations and predictive analytics solutions
by Ferran Garcia Pagans Neeraj Kharpate Henric Cronström James Richardson Philip HandKey Features Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Book Description Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business. This course begins by introducing you to the features and functions of the most modern edition of Qlik Sense so you get to grips with the application. The course will teach you how to administer the data architecture in Qlik Sense, enabling you to customize your own Qlik Sense application for your business intelligence needs. It also contains numerous recipes to help you overcome challenging situations while creating fully featured desktop applications in Qlik Sense. It explains how to combine Rattle and Qlik Sense Desktop to apply predictive analytics to your data to develop real-world interactive data applications. The course includes premium content from three of our most popular books: [*] Learning Qlik Sense: The Official Guide Second Edition [*] Qlik Sense Cookbook [*] Predictive Analytics using Rattle and Qlik Sense On completion of this course, you will be self-sufficient in improving your data analysis and will know how to apply predictive analytics to your datasets. Through this course, you will be able to create predictive models and data applications, allowing you to explore your data insights much deeper. What you will learn Build simple visualization models with Rattle and Qlik Sense Desktop Get to grips with the life cycle and new visualization functions of a Qlik Sense application Discover simple ways to examine data and get it ready for analysis Visualize your data with Qlik Sense's engaging and informative graphs Build efficient and responsive Associative Models Optimize Qlik Sense for sales, human resources, and demographic data discovery Explore various tips and tricks of navigation for the Qlik Sense® front end Develop creative extensions for your Qlik Sense® dashboard
Qlik Sense Cookbook: Over 80 recipes on data analytics to solve business intelligence challenges, 2nd Edition
by Philip Hand Neeraj Kharpate Pablo LabbeCreate dynamic dashboards to perform interactive analytics for business intelligence operationsKey FeaturesExplore newly added features in Qlik SenseDiscover best practices to work with data using Qlik SenseLearn to implement advanced functions for better data insightBook DescriptionQlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity. An expert Qlik Sense user can use its features for business intelligence in an enterprise environment effectively. Qlik Sense Cookbook is an excellent guide for all aspiring Qlik Sense developers and will empower you to create featured desktop applications to obtain daily insights at work.This book takes you through the basics and advanced functions of Qlik Sense February 2018 release. You’ll start with a quick refresher on obtaining data from data files and databases, and move on to some more refined features including visualization, and scripting, as well as managing apps and user interfaces. You will then understand how to work with advanced functions like set analysis and set expressions. As you make your way through this book, you will uncover newly added features in Qlik Sense such as new visualizations, label expressions and colors for dimension and measures.By the end of this book, you will have explored various visualization extensions to create your own interactive dashboard with the required tips and tricks. This will help you overcome challenging situations while developing your applications in Qlik Sense.What you will learnSource, preview, and distribute your data through interactive dashboardsExplore and work with the latest visualization functionsLearn how to write and use script subroutinesMake your UI advanced and intuitive with custom objects and indicatorsUse visualization extensions for your Qlik Sense dashboardWork with Aggr and learn to use it within set analysisWho this book is forQlik Sense Cookbook is for data and BI analysts who want to become well versed with Qlik Sense to apply business intelligence in data. If you are a beginner in data analytics and want to adopt an independent recipe-based approach to learn the required concepts and services in detail, this book is ideal! Individuals with prior knowledge of its sister product, QlikView, will also benefit from this book. Familiarity with the basics of business intelligence is a prerequisite.