Ironpython In Action

In 2005, Microsoft quietly announced an initiative to bring dynamic languages to the .NET platform. The starting point for this project was a .NET implementation of Python, dubbed IronPython. After a couple years of incubation, IronPython is ready for real-world use. It blends the simplicity, elegance, and dynamism of Python with the power of the .NET framework. IronPython in Action offers a comprehensive, hands-on introduction to Microsoft's exciting new approach for programming the .NET framework. It approaches IronPython as a first class .NET language, fully integrated with the .NET environment, Visual Studio, and even the open-source Mono implementation. You'll learn how IronPython can be embedded as a ready-made scripting language into C# and VB.NET programs, used for writing full applications or for web development with ASP. Even better, you'll see how IronPython works in Silverlight for client-side web programming. IronPython opens up exciting new possibilities. Because it's a dynamic language, it permits programming paradigms not easily available in VB and C#. In this book, authors Michael Foord and Christian Muirhead explore the world of functional programming, live introspection, dynamic typing and duck typing , metaprogramming, and more. IronPython in Action explores these topics with examples, making use of the Python interactive console to explore the .NET framework with live objects. The expert authors provide a complete introduction for programmers to both the Python language and the power of the .NET framework. The book also shows how to extend IronPython with C#, extending C# and VB.NET applications with Python, using IronPython with .NET 3.0 and Powershell, IronPython as a Windows scripting tool, and much more. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Practically and deeply understand concurrency in Python to write efficient programs About This Book Build highly efficient, robust, and concurrent applications Work through practical examples that will help you address the challenges of writing concurrent code Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly available Who This Book Is For This book is for Python developers who would like to get started with concurrent programming. Readers are expected to have a working knowledge of the Python language, as this book will build on these fundamentals concepts. What You Will Learn Explore the concept of threading and multiprocessing in Python Understand concurrency with threads Manage exceptions in child threads Handle the hardest part in a concurrent system — shared resources Build concurrent systems with Communicating Sequential Processes (CSP) Maintain all concurrent systems and master them Apply reactive programming to build concurrent systems Use GPU to solve specific problems In Detail Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices. Style and approach This easy-to-follow guide teaches you new practices and techniques to optimize your code, and then moves toward more advanced ways to effectively write efficient Python code. Small and simple practical examples will help you test the concepts yourself, and you will be able to easily adapt them for any application.

Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives

This is a hands-on guide that provides exemplary coverage of all the features and concepts related to PTVS. The book is intended for developers who are aiming to enhance their productivity in Python projects with automation tools that Visual Studio provides for the .Net community. Some basic knowledge of Python programming is essential.

To be a successful ASP.NET 4 developer, you need to know how to apply the vast array of new functionality available in the latest release of the .NET 4 Framework and Visual Studio 2010. This book will immerse you in a variety of advanced topics, including architecting different application data tiers, memory caching paradigms, data mining, and search engine optimization. Working through step-by-step exercises using P/LINQ, DLR, MEF, MVC, IronPython, Axum, and Ajax, you will learn a variety of approaches to building each of the key application tiers common to all web solutions. Using a proven technique of illustrating advanced concepts with functional solutions, all topics in the book are modeled on a fully operational content management system (CMS), built from the ground up. This ensures that you’ll be introduced to real-world examples that demonstrate the full functionality of the .NET 4 Framework for ASP.NET, and that you’ll be able to apply your new skills to any web development situation.