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- Integrieren Sie generative KI-Modelle effektiv in den Softwareentwicklungsprozess. - Lernen Sie, zielführende Prompts zu formulieren, die Ergebnisse der KI kritisch zu bewerten und deren Stärken optimal zu nutzen. - Erfahren Sie, wie Sie KI-gestützte Entwicklungswerkzeuge souverän einsetzen können, um Produktivität, Codequalität und Innovation zu steigern. - Mit Zusatzmaterial (Arbeitshilfen, Videos und Codebeispielen aus dem Buch) - Ihr exklusiver Vorteil: E-Book inklusive beim Kauf des gedruckten Buches Das Buch soll Softwareentwicklern eine praxisorientierte Einführung in die Zusammenarbeit mit generativer KI bieten, insbesondere beim Pair-Programming, wenn KI unterstützend zur Seite steht. Das Buch hat zum Inhalt, wie sich KI-Modelle, zum Beispiel ChatGPT, Copilot und ähnliche Tools, effektiv in den Entwicklungsprozess integrieren lassen, um Produktivität, Codequalität und Innovation zu fördern. Es geht daher primär um das Verständnis und den Nutzen dieser KI-Modelle, sowie um deren Anwendung als Tool und als Plattform, zum Beispiel über Schnittstellen (APIs). Zusätzlich werden unterschiedliche Closed und Open Source Modelle vorgestellt, da letztere beispielsweise einen Offline-Betrieb ermöglichen. AUS DEM INHALT // Warum KI im Pair-Programming?/Grundlagen der generativen KI/Einführung in SLMs und LLMs/Die Rolle von KI im Entwicklungsprozess/Lokale Nutzung von KI-Modellen/Pair-Programming mit KI/ /Open-Source-Modelle im Detail/KI und menschliche Interaktion: Ein Vergleich/ Fallstudien und Praxisbeispiele/Risiken, Herausforderungen und der Blick in die Zukunft/Werkzeugkasten für KI-gestütztes Pair-Programming

What will be the next revolution in software technology to follow XML and generics? Whatever it may be, it is likely to come from functional programming, where many of the key ideas of the last decade originated. In this textbook, the leading researchers of the field take you on a tour of the current hot topics in functional programming, with applications ranging from financial contracts to circuit design. There are also chapters on new idioms for structuring programs, such as monads and arrows. All new concepts are illustrated with many examples, and exercises appear throughout to further enliven the material. Several of the chapters describe a substantial piece of software, and most of these programs are collected on a website for free downloading. This book was edited in honour of Richard S.Bird, Professor of Computer Science at the University of Oxford, on his 60th birthday.

*Lua Programming Gems * / 44.49€ / Taschenbuch

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PrefaceIn recent years, Machine Learning and Data Science have revolutionized the way we understand and interact with data. From predictive analytics in finance and healthcare to real-time recommendation systems in e-commerce and streaming platforms, intelligent algorithms are now an integral part of the modern digital landscape. This book, "Machine Learning & Data Science: TensorFlow, PyTorch, XGBoost, Statsmodels," is crafted for learners and practitioners who aim to bridge the gap between theory and hands-on application using some of the most powerful tools in the industry. The rapid expansion of available data and computational power has made it possible to deploy increasingly complex models. However, success in this field requires more than just technical proficiency-it demands an understanding of the appropriate frameworks, their strengths, and the contexts in which they excel. This book is structured to serve that purpose. We explore TensorFlow and PyTorch, the two most widely adopted deep learning frameworks, each with its own philosophy and design choices. TensorFlow, with its scalable ecosystem and production-oriented approach, is ideal for building deployable machine learning systems. PyTorch, known for its intuitive design and dynamic computation graphs, is a favorite in the research community and for rapid prototyping. In contrast, XGBoost represents the pinnacle of gradient boosting techniques-efficient, scalable, and often the go-to choice for structured data and tabular modeling competitions. And then there's Statsmodels, a library that brings the richness of statistical modeling into the mix, enabling interpretability and insight that purely algorithmic models may lack. This book is designed with the following goals:To provide a comprehensive introduction to the foundational concepts of machine learning and data science. To illustrate practical implementations using TensorFlow, PyTorch, XGBoost, and Statsmodels through real-world examples and projects. To equip readers with the skills to choose and combine tools appropriately depending on the nature of the data and the problem at hand. To foster a deep understanding of not just how models work, but why they behave the way they do. Whether you are a student seeking to deepen your knowledge, a developer transitioning into the field, or a data scientist aiming to master additional tools, this book offers a balanced journey through both the statistical roots and the cutting-edge practices of machine learning. May this book serve not just as a manual, but as a roadmap in your data science journey-helping you think critically, implement confidently, and build responsibly. - The Author

Book Description The explosive growth of data in recent decades has transformed how we perceive problems, make decisions, and build intelligent systems. As industries across the globe embrace digital transformation, the demand for tools and techniques to extract meaningful insights from data has never been greater. This book, Machine Learning & Data Science: Scikit-learn is born out of that growing need-a practical, focused guide to foundational machine learning algorithms and their implementation using one of the most widely adopted libraries in Python: Scikit-learn . This book is designed for students, professionals, and enthusiasts seeking to build a strong conceptual and practical understanding of key machine learning techniques. Rather than overwhelming the reader with theory, we take a hands-on, example-driven approach centered on real-world applications and reproducible code. Each chapter builds from the ground up-explaining not just how an algorithm works, but why it behaves the way it does, and when to apply it effectively. We begin with core algorithms such as Linear Regression , Logistic Regression , and K-Nearest Neighbors (KNN) -laying the groundwork for predictive modeling and classification tasks. Next, we introduce model validation techniques like Cross-Validation and Grid Search , essential tools for evaluating and optimizing model performance. Building upon this foundation, we explore more complex algorithms like Decision Trees and Support Vector Machines (SVMs) , which offer greater flexibility and power in modeling nonlinear patterns. Additionally, we highlight the importance of feature scaling techniques like Min-Max normalization , which often determine the success of machine learning models. These techniques, though sometimes overlooked, are vital for ensuring that algorithms perform as expected and generalize well to unseen data. Throughout this book, we rely on Scikit-learn for implementation-not only because of its simplicity and power, but also because it exemplifies best practices in structuring machine learning workflows. Readers will gain practical experience with the tools and pipelines used by data scientists and machine learning practitioners in real projects. Whether you are taking your first steps into machine learning or looking to deepen your understanding of algorithmic foundations, this book provides a concise and reliable guide. May it serve as both a roadmap and a reference for your journey into the fascinating world of machine learning and data science. - The Author

Preface The world of software development is built upon two unshakable pillars: correctness and reliability. Writing code is only the beginning of the journey; ensuring that this code behaves as expected under all conditions is what distinguishes a working program from a trustworthy system. In Python programming, testing and debugging are not optional steps-they are integral practices that define the quality of the final product. This book, Python Programming: Testing & Debugging, was written with the goal of guiding programmers-whether beginners taking their first steps into structured testing or experienced developers refining their craft-through the essential tools and practices that transform ordinary scripts into dependable applications. The content of this book is structured around three fundamental components of Python's testing and debugging ecosystem: Pytest - a powerful, modern, and flexible framework that simplifies unit testing while supporting advanced features such as fixtures, parameterization, and plugins. Pytest has become the tool of choice for many Python developers, and this book explores its strengths from the basics to more sophisticated scenarios. Unittest - Python's built-in testing framework, which provides a solid, standardized foundation for writing test cases. By learning unittest, readers gain not only the ability to test effectively but also the benefit of understanding Python's philosophy of extensibility and convention-driven design. Pdb (Python Debugger) - a command-line tool that empowers developers to step into their code, inspect its state, and trace issues at runtime. Mastering Pdb helps programmers move beyond guesswork and into precise, efficient problem-solving. By weaving together these three strands-pytest, unittest, and pdb-this book provides a complete roadmap for developing resilient Python programs. Each chapter is crafted to balance explanation with practice: concepts are explained clearly, examples are chosen to be realistic and illustrative, and exercises are designed to challenge the reader to apply their knowledge in meaningful ways. The guiding philosophy throughout is that testing and debugging are not burdens to be endured, but skills to be embraced. They not only improve the quality of code but also enhance a programmer's confidence, efficiency, and creativity. With testing and debugging as companions, developers are free to experiment boldly, knowing that their safety net will catch errors before they cause harm. This book invites you to explore Python's rich ecosystem of testing and debugging tools, to practice their use, and to integrate them into your daily workflow. Whether you are building small scripts, large applications, or mission-critical systems, the practices you will learn here will serve you well throughout your programming journey. Author

*Programming * - Principles and Practice Using C++ / 74.49€ / ebook

*Pro Perl Programming * - From Professional to Advanced. First Edition / 69.49€ / Taschenbuch

Shell-Programmierung in Unix, Linux und OS X(die vierte Auflage von Kochan und Woods klassischem Unix-Shell-Programmier-Tutorial) kann jedem modernen Unix-, Linux- oder OS X-Benutzer helfen, mit dem Betriebssystem seiner Wahl schneller mehr zu erreichen. Es ist eines der weltweit angesehensten Unix-Programmierbücher und wurde durchgehend aktualisiert, um die heute weit verbreiteten Plattformen, einschließlich Oracle Solaria, Mac OS X und Linux, vollständig zu berücksichtigen.

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The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1 Knuths multivolume analysis of algorithms is widely recognized as the definitive description of classical computer science. The first three volumes of this work have long comprised a unique and invaluable resource in programming theory and practice. Scientists have marveled at the beauty and elegance of Knuths analysis, while practicing programmers have successfully applied his cookbook solutions to their day-to-day problems. The level of these first three volumes has remained so high, and they have displayed so wide and deep a familiarity with the art of computer programming, that a sufficient review of future volumes could almost be: Knuth, Volume n has been published. Data Processing Digest Knuth, Volume n has been published, where n = 4A. In this long-awaited new volume, the old master turns his attention to some of his favorite topics in broadword computation and combinatorial generation (exhaustively listing fundamental combinatorial objects, such as permutations, partitions, and trees), as well as his more recent interests, such as binary decision diagrams. The hallmark qualities that distinguish his previous volumes are manifest here anew: detailed coverage of the basics, illustrated with well-chosen examples; occasional forays into more esoteric topics and problems at the frontiers of research; impeccable writing peppered with occasional bits of humor; extensive collections of exercises, all with solutions or helpful hints; a careful attention to history; implementations of many of the algorithms in his classic step-by-step form. There is an amazing amount of information on each page. Knuth has obviously thought long and hard about which topics and results are most central and important, and then, what are the most intuitive and succinct ways of presenting that material. Since the areas that he covers in this volume have exploded since he first envisioned writing about them, it is wonderful how he has managed to provide such thorough treatment in so few pages. Frank Ruskey, Department of Computer Science, University of Victoria The book is Volume 4A, because Volume 4 has itself become a multivolume undertaking. Combinatorial searching is a rich and important topic, and Knuth has too much to say about it that is new, interesting, and useful to fit into a single volume, or two, or maybe even three. This book alone includes approximately 1500 exercises, with answers for self-study, plus hundreds of useful facts that cannot be found in any other publication. Volume 4A surely belongs beside the first three volumes of this classic work in every serious programmers library. Finally, after a wait of more than thirty-five years, the first part of Volume 4 is at last ready for publication. Check out the boxed set that brings together Volumes 1 - 4A in one elegant case, and offers the purchaser a $50 discount off the price of buying the four volumes individually. p> The...

"With this ... guide, experienced systems programmers will learn how to successfully bridge the gap between performance and safety using Rust"--Back cover.

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*Math for Programming * / 46.49€ / Taschenbuch

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Programmierkabel bietet ein dediziertes Verbindungszubehör für Vogel's RISE Monitorhalterungs-Setups. Vogel's RISE A151 PROGRAMMING CABLE ist als Monitorhalterungszubehör innerhalb der Produktreihe Vogel's ProAV Solutions konzipiert und unterstützt Einrichtungs- und Konfigurationsanforderungen, bei denen eine Programmierverbindung erforderlich ist. Marke: Vogel's Kategorie: ProAV Solutions Zubehörtyp: Monitor Mount Accessories Integriert sich sauber in das Vogel's RISE Zubehör-Ökosystem für eine unkomplizierte Systemhandhabung.

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