Scripting Languages

Eine umfassende Einführung in JavaScript für alle, die professionell JavaScript programmieren lernen möchten! . Alle Grundlagen von JavaScript werden anhand zahlreicher Beispiele einfach nachvollziehbar erklärt . Viele weiterführende Themen vertiefen das Verständnis der Programmiersprache und helfen auch komplexere Seiten zu entwickeln: OOP, AJAX, NodeJS, UnitTests, Server Side Rendering, u.v.m. . Dank des lockeren Schreibstils und der vielen Übungsaufgaben und Beispielprojekte wird der Lernerfolg garantiert . Zielgruppe: Webentwickler, Webdesigner Programmiereinsteiger und -umsteiger, professionelle Programmierer . Platzierung: Webentwicklung, JavaScript

Inhalt <BR>- Kernkonzepte: Nutzen Sie den interaktiven Python-Interpreter und die Kommandozeile, um die Grundlagen zu erlernen. <BR>- Objektorientierter Code: Schreiben Sie objektorientierten Code mit den nativen Python-Objekten. <BR>- Eigenständige Pakete: Entwickeln und veröffentlichen Sie Ihre eigenen Python-Pakete. <BR>- Funktionale Programmierung: Verwenden Sie elegante funktionale Techniken wie Python Comprehensions. <BR>- Testgetriebene Entwicklung (TDD): Erweitern Sie Ihre Fähigkeiten, indem Sie neue Objekte mit TDD erstellen. <BR>- Shell-Scripting: Nutzen Sie die außergewöhnlichen Shell-Scripting-Fähigkeiten von Python. <BR>- Webanwendungen: Erstellen und bereitstellen Sie vollständige Webanwendungen mit Routen, Layouts, Vorlagen und Formularen. <BR>- Data Science: Starten Sie Ihre Reise in die Welt der Datenanalyse, Visualisierung und maschinellen Lernens.

Learning a new language by following video tutorials, blog posts, and documentation is a tiresome activity.

JavaScript gives web developers great power to create rich interactive browser experiences, and much of that power is provided by the browser itself. Modern web APIs enable web-based applications to come to life like never before, supporting actions that once required browser plug-ins. Some are still in an experimental stage, but many are ready for use today. With this hands-on cookbook, author Joe Attardi helps you explore the powerful APIs available in modern browsers and guides you through the specific tasks that they unlock. Because these features are web standards, there is comprehensive documentation available from trusted resources such as MDN Web Docs. The knowledge you'll gain is transferable across different companies and projects. - Learn the breadth of functionality available in modern browser APIs - Explore future APIs that are still in an experimental stage - Discover newer elements, such as dialog that replaces the need for a third-party library - Build more powerful and interactive web applications using native APIs - Understand the permissions model used by the browser to unlock functionality such as geolocation and push notifications

- Alle Python-Grundlagen, die Sie für Ihren Raspberry Pi 5, 4, 3 oder Zero brauchen- Projekte mit Sensoren, Relais und AD-Wandlern- Einsatz von Peripheriegeräten wie Kameramodul und LautsprecherRaspberry Pi - der Mini-Computer zum ExperimentierenDer Raspberry Pi ist ein preiswerter Computer in der Größe einer Kreditkarte. In Kombination mit der Programmiersprache Python bietet er eine hervorragende Umgebung für die schnelle Realisierung technischer Ideen und Projekte. In diesem Buch lernen Sie alle Grundlagen, die Sie brauchen, um mit dem Raspberry Pi oder dem Raspberry Pi Zero spannende Projekte zu realisieren.Zahlreiche Projekte mit Sensoren, Kamera u.v.m.Sie finden in diesem Buch eine Fülle von kleinen Projekten, die auf die besondere Hardware des Raspberry Pi und das Linux-Betriebssystem Raspberry Pi OS zugeschnitten sind. Dazu gehören sowohl Schaltungen mit Sensoren (Temperatur, Licht, Alkohol), Relais, AD-Wandlern und LEDs als auch die Verwendung des Kameramoduls und des 1-Wire- sowie SPI-Bus zur Datenkommunikation. Zahlreiche Illustrationen und einfache Beispiele zum Ausprobieren erleichtern das Verständnis.Die Programmiersprache Python als GrundlagePython ist - auch für Programmiereinsteiger - einfach zu lernen und deshalb Teil des Gesamtkonzepts des Raspberry Pi. Dieses Buch vermittelt Ihnen anhand anschaulicher Beispiele sowohl die Grundlagen von Python als auch fortgeschrittene Techniken wie Objektorientierung, Internetprogrammierung und grafische Benutzungsoberflächen. Am Ende jedes Kapitels finden Sie Aufgaben mit Lösungen zum Download, mit denen Sie Ihr Wissen festigen, erweitern und vertiefen können.Aus dem Inhalt:- Gerätesteuerung- Messwerterfassung und -visualisierung- Interaktive Spiele mit Schaltern und Sensoren- Animationen und Simulationen- Automatische Textproduktion- Digitale Bildverarbeitung- Morsezeichen senden und Signalmuster erkennen- Projekte mit dem Sense HAT- Farben und Bewegung wahrnehmen- Steuerung mit Potentiometer und AD-Wandler- Auswertung von Daten aus dem Internet- Webserver und WebcamZum Download: Programmcode für alle Projekte sowie Lösungen der Übungsaufgaben

This book provides an introduction to both heterogeneous execution and managed runtime environments (MREs) by discussing the current trends in computing and the evolution of both hardware and software. To this end, it first details how heterogeneous hardware differs from traditional CPUs, what their key components are and what challenges they pose to heterogenous execution. The most ubiquitous ones are General Purpose Graphics Processing Units (GPGPUs) which are pervasive across a plethora of application domains ranging from graphics processing to training of AI and Machine Learning models. Subsequently, current solutions on programming heterogeneous MREs are described, highlighting for each current existing solution the associated advantages and disadvantages. This book is written for scientists and advanced developers who want to understand how choices at the programming API level can affect performance and/or programmability of heterogeneous hardware accelerators, how toimprove the underlying runtime systems in order to seamlessly integrate diverse hardware resources, or how to exploit acceleration techniques from their preferred programming languages.

Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That's where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect.Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You'll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You'll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications.After completing this book, you'll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making.What You Will LearnCreate synthetic tabular data with R and PythonUnderstand how synthetic data is important for artificial neural networksMaster the benefits and challenges of synthetic dataUnderstand concepts such as domain randomization and domain adaptation related to synthetic data generationWho This Book Is ForThose who want to learn about synthetic data and its applications, especially professionals working in the field of machine learning and computer vision. This book will also be useful for graduate and doctoral students interested in this subject.

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Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence.The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, you'll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios.The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI's profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions. What You Will LearnUnderstand the principles of responsible AI and their importance in today's digital worldMaster techniques to detect and mitigate bias in AIExplore methods and tools for achieving transparency and explainabilityDiscover best practices for privacy preservation and security in AIGain insights into designing robust and reliable AI modelsWho This Book Is ForAI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI

Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development.<BR> <BR> The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI.<BR> <BR> After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.<BR>What You Will Learn- Know the basic concepts of optimization tuners, search space, and trials- Apply different hyper-parameter optimization algorithms to develop effective neural networks- Construct new deep learning models from scratch- Execute the automated Neural Architecture Search to create state-of-the-art deep learning models- Compress the model to eliminate unnecessary deep learning layers<BR>Who This Book Is For <BR>Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development