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Purely Functional Data Structures
by Chris OkasakiMost books on data structures assume an imperative language such as C or C++. However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.
Purple Team Strategies: Enhancing global security posture through uniting red and blue teams with adversary emulation
by David Routin Simon Thoores Samuel RossierLeverage cyber threat intelligence and the MITRE framework to enhance your prevention mechanisms, detection capabilities, and learn top adversarial simulation and emulation techniquesKey FeaturesApply real-world strategies to strengthen the capabilities of your organization's security systemLearn to not only defend your system but also think from an attacker's perspectiveEnsure the ultimate effectiveness of an organization's red and blue teams with practical tipsBook DescriptionWith small to large companies focusing on hardening their security systems, the term "purple team" has gained a lot of traction over the last couple of years. Purple teams represent a group of individuals responsible for securing an organization's environment using both red team and blue team testing and integration – if you're ready to join or advance their ranks, then this book is for you.Purple Team Strategies will get you up and running with the exact strategies and techniques used by purple teamers to implement and then maintain a robust environment. You'll start with planning and prioritizing adversary emulation, and explore concepts around building a purple team infrastructure as well as simulating and defending against the most trendy ATT&CK tactics. You'll also dive into performing assessments and continuous testing with breach and attack simulations.Once you've covered the fundamentals, you'll also learn tips and tricks to improve the overall maturity of your purple teaming capabilities along with measuring success with KPIs and reporting.With the help of real-world use cases and examples, by the end of this book, you'll be able to integrate the best of both sides: red team tactics and blue team security measures.What you will learnLearn and implement the generic purple teaming processUse cloud environments for assessment and automationIntegrate cyber threat intelligence as a processConfigure traps inside the network to detect attackersImprove red and blue team collaboration with existing and new toolsPerform assessments of your existing security controlsWho this book is forIf you're a cybersecurity analyst, SOC engineer, security leader or strategist, or simply interested in learning about cyber attack and defense strategies, then this book is for you. Purple team members and chief information security officers (CISOs) looking at securing their organizations from adversaries will also benefit from this book. You'll need some basic knowledge of Windows and Linux operating systems along with a fair understanding of networking concepts before you can jump in, while ethical hacking and penetration testing know-how will help you get the most out of this book.
Pursuit of the Universal
by Arnold Beckmann Laurent Bienvenu Nataša JonoskaThis book constitutes the refereed proceedings of the 12th Conference on Computability in Europe, CiE 2016, held in Paris, France, in June/July 2016. The 18 revised full papers and 19 invited papers and invited extended abstracts were carefully reviewed and selected from 40 submissions. The conference CiE 2016 has six special sessions - two sessions, cryptography and information theory and symbolic dynamics, are organized for the first time in the conference series. In addition to this new developments in areas frequently covered in the CiE conference series were addressed in the following sessions: computable and constructive analysis; computation in biological systems; history and philosophy of computing; weak arithmetic.
Pusheen the Cat's Guide to Everything (I Am Pusheen)
by Claire BeltonPusheen the Cat is back with a brand-new collection of adorable comics, expert advice, and silly antics featuring Pusheen and all her friends! <p><p> Whether you’re hoping to learn how to tell if your cat is a Vampurr or looking to study a comprehensive guide to being lazy—Pusheen has got you covered in this super cute guide to everything! This delightful collection of comics and illustrations features some of the most popular and purr-fectly adorable Pusheen comics you know and love, plus a healthy serving of never-before-seen material. Pusheen the Cat has charmed millions of fans worldwide with her humor, bounces, and tail wiggles. Join in on the fun with this super cute collection perfect for cat lovers and comics fans alike!
Pushing Pixels: Chris Georgenes’ Secret Weapons for the Modern Flash Animator
by Chris GeorgenesYou've got the cheats, tutorials, and how-tos. What else do you need? Go above and beyond those stop-gaps and step-by-steps with Pushing Pixels, the real-world guide to developing dynamic and fun content from conception to deployment. Whether you are animating for a short, a fun cartoon, or a mobile game, renowned Flash expert Chris Georgenes will show you his approach with various types of animation projects, from start to finish. Providing in-depth knowledge of the little-known secrets used by the pros to produce creative, professional animations, this is the go-to source for anyone looking to create great animation.
Putting Balloons on a Wall Is Not a Book: Inspirational Advice (and Non-Advice) for Life from @blcksmth
by Michael James SchneiderFrom viral balloon-word artist and Instagram sensation Michael James Schneider (@blcksmth) comes a one-of-a-kind debut gift book with never-before-seen original artwork!Featuring many of @blcksmth&’s most iconic balloon, flower, and light installations—plus exclusive new content—this book has a little something for everyone. Filled with funny, inspiring, and heartwarming messages on topics like self-love, self-growth, self-doubt, and advice for your future self, this is the perfect gift for any occasion or reader.
Putting Ourselves Back in the Equation: Why Physicists Are Studying Human Consciousness and AI to Unravel the Mysteries of the Universe
by George MusserDistant galaxies, dark matter, black holes – elusive, incomprehensible and inhospitable – these are the building blocks of modern physics. But where do we fit in this picture? For centuries, we have separated mind from matter. While physicists have pursued a theory of &‘everything&’ with single-minded purpose, the matter of the mind, of human consciousness, has been conveniently sidestepped and ignored – consigned to priests, philosophers and poets. With the ambition of Stephen Hawking, Carlo Rovelli and Brian Cox, Putting Ourselves Back in the Equation sets out a bold new vision for theoretical physics, unrestricted by sleek equations and neat formulations. Combining cutting-edge neuroscience with the latest in quantum mechanics, acclaimed writer Musser offers a new interpretation of human consciousness. From bizarre cognitive phenomena, like lucid dreaming and self-taught synaesthesia, to the latest technological developments in AI, Musser asks: what can physics teach us about what it means to be human?
Putting People On The Map: Protecting Confidentiality With Linked Social-spatial Data
by National Research Council of the National AcademiesPrecise, accurate spatial information linked to social and behavioral data is revolutionizing social science by opening new questions for investigation and improving understanding of human behavior in its environmental context. At the same time, precise spatial data make it more likely that individuals can be identified, breaching the promise of confidentiality made when the data were collected. Because norms of science and government agencies favor open access to all scientific data, the tension between the benefits of open access and the risks associated with potential breach of confidentiality pose significant challenges to researchers, research sponsors, scientific institutions, and data archivists. Putting People on the Map finds that several technical approaches for making data available while limiting risk have potential, but none is adequate on its own or in combination. This book offers recommendations for education, training, research, and practice to researchers, professional societies, federal agencies, institutional review boards, and data stewards.
Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation (Lecture Notes in Social Networks)
by Jalal Kawash Reda Alhajj Mehmet Kaya Şuayip BirinciThis book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this wide coverage, the book forms a good source for practitioners and researchers, including instructors and students.
Putting the Local in Global Education: Models for Transformative Learning Through Domestic Off-Campus Programs
by Neal W. SobaniaThe position taken in this volume is that domestic off-campus study can be just as powerful a transformative learning experience as study overseas, and that domestic programs can equally expand students’ horizons, their knowledge of global issues and processes, their familiarity and experience with cultural diversity, their intercultural skills, and sense of citizenship.This book presents both the rationale for and examples of “study away”, an inclusive concept that embraces study abroad while advocating for a wide variety of domestic study programs, including community-based education programs that employ academic service-learning and internships.With the growing diversification—regionally, demographically, culturally, and socio-economically—of developed economies such as the US, the local is potentially a “doorstep to the planet” and presents opportunities for global learning. Moreover, study away programs can address many of the problematic issues associated with study abroad, such as access, finance, participation, health and safety, and faculty support. Between lower costs, the potential to increase the participation of student cohorts typically under-represented in study abroad, the lowering of language barriers, and the engagement of faculty whose disciplines focus on domestic issues, study at home can greatly expand the reach of global learning.The book is organized in five sections, the first providing a framework and the rationale for domestic study way programs; addressing administrative support for domestic vs. study abroad programs; exploring program goals, organization, structure, assessment and continuous improvement; and considering the distinct pedagogies of experiential and transformative education.The second section focuses on Semester Long Faculty Led Programs, featuring examples of programs located in a wide variety of locations – from investigations into history, immigration, culture, and the environment through localities in the West and the Lowcountry to exploring globalization in L.A and New York. Section three highlights five Short Term Faculty Led Programs. While each includes an intensive immersive study away experience, two illustrate how a 7 – 10 day study away experience can be effectively embedded into a regular course taught on campus. The fourth section, on Consortium Programs, describes programs that are either sponsored by a college that makes its program available to consortium members and non-members, or is offered by an independent non-for-profit to which institutions send their students. The final section on Community Engagement and Domestic Study Away addresses the place of community-based education in global learning and provides examples of academic programs that employ service-learning as a tool for collaborative learning, focusing on issues of pedagogy, faculty development and the building long-term reciprocal relationship with community partners to co-create knowledge.The book is intended for study abroad professionals, multicultural educators, student affairs professionals, alternative spring break directors, and higher education administrators concerned about affordably expanding global education opportunities.
PySide GUI Application Development
by Venkateshwaran LoganathanAn accessible and practical guide to developing GUI's for Python applications.This book is written for Python programmers who want to learn about GUI programming. It is also suitable for those who are new to Python but are familiar with object-oriented programming.
PySide GUI Application Development - Second Edition
by Venkateshwaran Loganathan Gopinath JaganmohanDevelop more dynamic and robust GUI applications using PySide, an open source cross-platform UI framework About This Book * Designed for beginners to help you get started with GUI application development * Develop your own applications by creating customized widgets and dialogs * Written in a simple and elegant structure so you easily understand how to program various GUI components Who This Book Is For This book is written for Python programmers who want to learn about GUI programming. It is also suitable for those who are new to Python but are familiar with object-oriented programming. What You Will Learn * Program GUI applications in an easy and efficient way * Download and install PySide, a cross-platform GUI development toolkit for Python * Create menus, toolbars, status bars, and child windows * Develop a text editor application on your own * Connect your GUI to a database and manage it * Execute SQL queries by handling databases In Detail Elegantly-built GUI applications are always a massive hit among users. PySide is an open source software project that provides Python bindings for the Qt cross-platform UI framework. Combining the power of Qt and Python, PySide provides easy access to the Qt framework for Python developers and also acts as an excellent rapid application development platform. This book will take you through everything you need to know to develop UI applications. You will learn about installing and building PySide in various major operating systems as well as the basics of GUI programming. The book will then move on to discuss event management, signals and slots, and the widgets and dialogs available with PySide. Database interaction and manipulation is also covered. By the end of this book, you will be able to program GUI applications efficiently and master how to develop your own applications and how to run them across platforms. Style and approach This is an accessible and practical guide to developing GUIs for Python applications.
PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
by Tomasz Drabas Denny LeeCombine the power of Apache Spark and Python to build effective big data applicationsKey FeaturesPerform effective data processing, machine learning, and analytics using PySparkOvercome challenges in developing and deploying Spark solutions using PythonExplore recipes for efficiently combining Python and Apache Spark to process dataBook DescriptionApache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.What you will learnConfigure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environmentsCreate DataFrames from JSON and a dictionary using pyspark.sqlExplore regression and clustering models available in the ML moduleUse DataFrames to transform data used for modelingConnect to PubNub and perform aggregations on streamsWho this book is forThe PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.
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.
Pythagorean Fuzzy Sets: Theory and Applications
by Harish GargThis book presents a collection of recent research on topics related to Pythagorean fuzzy set, dealing with dynamic and complex decision-making problems. It discusses a wide range of theoretical and practical information to the latest research on Pythagorean fuzzy sets, allowing readers to gain an extensive understanding of both fundamentals and applications. It aims at solving various decision-making problems such as medical diagnosis, pattern recognition, construction problems, technology selection, and more, under the Pythagorean fuzzy environment, making it of much value to students, researchers, and professionals associated with the field.
Python
by James O. KnowltonThis project-based, hands-on book is designed to show you how to use Python to create scripts that are easy to maintain and enhance. Taking a real-world approach, the book explains how Python can be used to solve programming problems. It includes a Python refresher or primer for programmers new to Python. The code provided in the book is simplistic or trivial, but is effective in walking you through the process of creating robust scripts that you can use immediately to create real solutions to the challenges you may face.<P><P> Advisory: Bookshare has learned that this book offers only partial accessibility. We have kept it in the collection because it is useful for some of our members. To explore further access options with us, please contact us through the Book Quality link on the right sidebar. Benetech is actively working on projects to improve accessibility issues such as these.
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.
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 2.6 Graphics Cookbook
by Mike Ohlson FineThis book has recipes that show enthusiastic users how easy graphic programming can be. Simple explanations in plain English are used. The recipes are built up, in each chapter, starting as simply as possible and moving to more complex programs with which you can comfortably create 2D vector graphics and animations. You will learn how to combine both vector and photo images seamlessly! If you are looking to create animated graphics to represent real-world scenarios then this book is for you. Teachers, scholars, students, and engineers who know it is possible to make fascinating models and demonstrations but have not found a handbook that pulls it all together in one place will find what they need in this recipe bank. Basic knowledge of Python programming is required and access to the Web and Google will be useful.