Programmierung
Cloud-Infrastrukturen (2. Auflage)
Cloud-Infrastrukturen. Das Handbuch für DevOps-Teams und Administratoren (2., aktualisierte Auflage, 2025)Infrastructure as a Service: Moderne IT-Infrastrukturen werden in die Cloud integriert und nutzen die Rechenzentren von Amazon, Microsoft und Co. Das ermöglicht ganz neue Arbeitsweisen und verspricht mühelose Skalierbarkeit und eine einfache Administration – aber der Teufel steckt im Detail!Wo AWS, Azure und andere Anbieter echte Vorteile ausspielen können, welche Probleme sich dort verstecken und wie Sie die Dienste richtig nutzen, zeigen Ihnen Kevin Welter und Daniel Stender anhand vieler Praxisbeispiele und Anleitungen in diesem Handbuch.Welcome to the Cloud!1. Public Cloud ComputingDie Vorteile von IaaS kennen Sie: Kostenersparnis, flexible Skalierbarkeit, Ressourcen nach Bedarf. Wie Sie mit den richtigen Konzepten das Maximum aus der Cloud herausholen, erfahren Sie hier.2. Der Werkzeugkasten für Cloud EngineersIn der modernen Systemadministration führt kein Weg mehr an zeitgemäßen Tools vorbei. Setzen Sie Python, Google Go und Ansible ein, um Ihre Cloud-Infrastruktur effizient zu verwalten. Und benutzen Sie Docker und Kubernetes als Plattform für Ihre Applikationen in der Cloud.3. Infrastructure-as-CodeFür das Cloud Computing brauchen Sie die richtigen Werkzeuge und Ideen. So scripten Sie Ihre Infrastruktur und sorgen für komfortable Automation und sichere Reproduzierbarkeit.Aus dem InhaltPublic Cloud Computing: IaaS und PaaSWerkzeuge und Skills für Cloud EngineersIaaS-Anbieter verwendenAmazon Web Services, Azure, Google Compute Engine, Hetzner und andere: Welcher Anbieter ist der Richtige für mich?Hybrid- und Multicloud-ComputingCloud-Infrastrukturen automatisch ausrollenCloud-Infrastrukturen mit Ansible konfigurierenCloud-Server testenCloud-Monitoring mit PrometheusCloud-Ressourcen programmierenLeseprobe (PDF-Link)Über die AutorenKevin Welterist Mitgründer und Geschäftsführer der HumanITy GmbH. Mit seinem Unternehmen unterstützt er Großkonzerne wie Deutsche Bahn, EnBW und Deutsche Telekom bei der Weiterentwicklung ihrer Software sowie der dazugehörigen Prozesse.Daniel Stenderhat als freier Cloud-, DevOps- und Linux-Engineer für Banken und Finanzdienstleister gearbeitet.
Schrödinger programmiert Python (2. Auflg.)
Schrödinger programmiert Python, das etwas andere Fachbuch (2. Auflage, 2025)Vom Feinsten! Lerne Python so, dass es Spaß macht und du alles behältst. Mit Schrödinger tauchst du in die vielseitige Sprache voll ein und schreibst von Anfang an guten Python-Code. Von der Installation über GUIs bis zur Einbindung von Bibliotheken, z. B. für Diagramme lernst Du alles, was Du über Python wissen musst. Und die vielen Aufgaben und Übungen sorgen dafür, dass das Gelernte auch richtig sitzt. Für Einsteiger, Umsteiger und Fans. Mit unterhaltsamen Illustrationen.Schrödinger garantiert1. Gründlicher EinstiegSchrödinger ist zwar ein etwas ungeduldiger Typ, aber Profi Stephan Elter bringt Ruhe und System in die Sache: Erst einmal alles installieren und lernen, wie man Dateien abspeichert, wie die Syntax aussieht und was es mit dem »Zen of Python« auf sich hat.2. Lesen, Mitmachen, ÜbenZu jedem Thema gibt es nach einer Einführung etwas für euch zu tun. Mal entsteht eine Anwendung, mal repariert oder verbessert ihr Code. Und um sicher zu gehen, dass ihr alles verstanden habt, gibt es außerdem Übungsaufgaben mit Lösungen.3. Gute Fragen, gute AntwortenPython ist unvorstellbar vielseitig. Ideal für den Einstieg wie für die Wissenschaft, objektorientiert und funktional, für kleine Skripte, komplexe Software oder neuronale Netze. Der Sprachkern ist dabei immer der gleiche, so dass ihr für alles gut gerüstet seid.Leseprobe (PDF-Link)Das alles (und noch viel mehr):Installationshilfe und gründlicher EinstiegObjektorientierung aus der Profi-PraxisDas Zen of PythonUmgang mit Zeit und DatumTeile und herrsche: ModuleAlles rund um DatenbankenKeine Angst vor regulären AusdrückenSchrödinger probiert Künstliche Intelligenz mit Python ausUnmengen an Beispielcode und ÜbungenÜber den Autor:Stephan Elter würde nie ein trockenes Fachbuch schreiben. Er entwickelt Software mit Python, Java, JavaScript, PHP und anderen Sprachen.
Neuronale Netze programmieren mit Python (3. Auflg.)
Geniale Ideen einfach erklärt. Das Praxisbuch für Einsteiger in 3. Auflage aus dem Januar 2025.Neuronale Netze sind die Technologie hinter Deep Learning, Machine Learning und generativer Künstlicher Intelligenz wie ChatGPT. Sie revolutionieren derzeit die verschiedensten Anwendungsgebiete vom Strategiespiel bis zur Bild- und Spracherkennung. In neuronalen Netzen stecken geniale Ideen, die sich zum Glück einfach erklären lassen. Unsere Experten helfen Ihnen dabei, neuronale Netze zu verstehen und selber zu entwickeln. Um sie gewinnbringend einzusetzen, programmieren Sie verschiedene Netztypen selbst nach. Und zwar in Python, der Hauptsprache der KI-Welt. Sie werden sich dabei mit Mathematik und Programmierung befassen, brauchen aber keine konkreten Vorkenntnisse. Roland Schwaiger und Joachim Steinwendner erklären Ihnen alles besonders anschaulich mit zahlreichen Abbildungen. Ein faszinierendes Buch, das Ihnen den Durchblick in der KI-Welt bringt.Schritt für Schritt zum trainierten KI-Modell: Grundlagen Für Ihren leichten Einstieg haben die Autoren eine Lernumgebung vorbereitet, erläutern alle mathematischen Konzepte von Grund auf und fangen mit einfachen Neuronalen Netzen an. Python-Crashkurs inklusive. Praxis, Praxis, Praxis Die Theorie ist in diesem Fall wirklich wichtig. Aber dann entwickeln Sie selbst, und zwar verschiedene Netztypen – und wenden sie auf verschiedene Fragestellungen an. Die Maschinen lernen lassen Neuronale Netze müssen trainiert werden, und dabei kommt es auf das Wie an! Lernen Sie direkt am Beispiel, Fallstricke zu umgehen, Trainingsdaten geschickt zu nutzen, Werkzeuge auszuwählen und die Trefferquoten ihrer Modelle zu erhöhen. Aus dem Inhalt: Die Grundidee hinter Neuronalen Netzen Ein einfaches Neuronales Netz aufbauen Neuronale Netze trainieren Überwachtes und unüberwachtes Lernen Wichtige mathematische Grundlagen Reinforcement Learning Verschiedene Netzarten und ihre Anwendungsbereiche Back Propagation Deep Learning Programmieren von Transformer-Netzen Einführung in TensorFlow Ethische, rechtliche und ökologische Fragen Leseprobe (PDF-Link) Die Autoren:Dr. Roland Schwaiger ist in so manchen Bereichen der IT-Welt zuhause – Entwickler, Dozent, Forscher und Autor.Prof. Dr. Joachim Steinwendner ist Forschungsfeldleiter für Digital GeoHealth an der Fernfachhochschule Schweiz mit einer fundierten Expertise in Data Science, Maschinellem Lernen, Empfehlungssystemen und Deep Learning.
ABAP Cloud
Entdecken Sie die Zukunft der ABAP-Entwicklung – direkt in der Cloud! Programmieren Sie eigenständige ABAP-Anwendungen und Erweiterungen für Ihre SAP-S/4HANA-Lösungen, ohne in den Standard-Code eingreifen zu müssen. Mit diesem praktischen Handbuch lernen Sie die ABAP-Umgebungen der SAP Business Technology Platform und von SAP S/4HANA Cloud kennen und erfahren alles über den verfügbaren Sprachumfang und die Arbeit mit dem ABAP RESTful Application Programming Model. Aus dem Inhalt: SAP Business Technology PlatformSAP S/4HANA CloudABAP Development ToolsABAP RESTful Application Programming ModelSAP Fiori ElementsCore Data ServicesCustomizingSoftware Lifecycle ManagementABAP Cloud für On-Premise-Systeme Einleitung ... 17 TEIL I. Einführung in das neue Entwicklungsmodell für ABAP ... 23 1. Die SAP-Anwendungsentwicklung im Wandel ... 25 1.1 ... Herausforderungen des digitalen Zeitalters ... 26 1.2 ... Cloud-Computing und die SAP Business Technology Platform ... 33 1.3 ... Konzepte der SAP Business Technology Platform ... 36 1.4 ... Cloud Foundry als Basis für das ABAP Environment ... 45 1.5 ... Dienste der SAP Business Technology Platform ... 49 1.6 ... Zusammenfassung ... 52 2. ABAP-Plattform ... 55 2.1 ... SAP S/4HANA Cloud und on premise ... 56 2.2 ... SAP Business Technology Platform, ABAP Environment ... 65 2.3 ... Einordnung in die SAP-Systemlandschaft ... 76 2.4 ... Zusammenfassung ... 77 3. Das ABAP-Cloud-Entwicklungsmodell ... 79 3.1 ... Evolution der ABAP-Programmiermodelle ... 79 3.2 ... Was ist ABAP Cloud? ... 82 3.3 ... Freigegebener Sprachumfang und Objekte ... 85 3.4 ... Zusammenfassung ... 100 4. Die Arbeitsumgebung einrichten ... 101 4.1 ... Das Dashboard als Administrationsoberfläche ... 101 4.2 ... Identitäts- und Zugriffsverwaltung ... 104 4.3 ... ABAP Development Tools als Entwicklungsumgebung einrichten ... 107 4.4 ... Zusammenfassung ... 113 TEIL II. Entwicklung von Geschäftsobjekten und Erweiterung von SAPAnwendungen ... 115 5. Aufbau des Datenmodells der Beispielanwendung ... 117 5.1 ... Einführung in die Beispielanwendung ... 118 5.2 ... Softwarekomponenten ... 120 5.3 ... Datenmodell implementieren ... 131 5.4 ... Eine Konsolenanwendung anlegen ... 143 5.5 ... Virtuelles Datenmodell ... 147 5.6 ... Zusammenfassung ... 155 6. Geschäftsobjekte modellieren ... 157 6.1 ... ABAP RESTful Application Programming Model als Grundlage für die Geschäftsobjektmodellierung ... 157 6.2 ... Aufbau des Geschäftsobjekts ... 160 6.3 ... Geschäftsobjektmodellierung mittels CDS-Views ... 163 6.4 ... Business-Services erstellen ... 167 6.5 ... Zusammenfassung ... 180 7. Geschäftslogik entwickeln ... 181 7.1 ... Implementierungsszenarien ... 181 7.2 ... Transaktionales Verhalten implementieren ... 186 7.3 ... Nummerierung implementieren ... 198 7.4 ... Entity Manipulation Language ... 208 7.5 ... Ermittlungen implementieren ... 214 7.6 ... Validierungen implementieren ... 221 7.7 ... Aktionen implementieren ... 227 7.8 ... Zusätzliche Feldeigenschaften definieren ... 231 7.9 ... Sperrkonzepte ... 238 7.10 ... Zusammenfassung ... 244 8. Erweiterung des Speicherverhaltens ... 247 8.1 ... Erweitertes Speicherverhalten implementieren ... 248 8.2 ... Eigene Speicherroutinen implementieren ... 256 8.3 ... Zusammenfassung ... 262 9. Verwendung von Geschäftsobjekten ... 263 9.1 ... Flexible Konsumierung von Geschäftsobjekten mittels Projektionsschicht ... 264 9.2 ... Projektionsschicht erstellen ... 267 9.3 ... Virtuelle Elemente modellieren ... 278 9.4 ... Geschäftsobjekte freigeben ... 282 9.5 ... Zusammenfassung ... 290 10. Gestalten von Benutzeroberflächen ... 293 10.1 ... Einheitliche Benutzeroberflächen mit SAP Fiori Elements ... 294 10.2 ... UI-Annotationen ... 300 10.3 ... Metadatenerweiterungen ... 325 10.4 ... Aktive Annotationen ... 327 10.5 ... Benutzeroberflächen mit dem SAP Business Application Studio anlegen ... 329 10.6 ... Benutzeroberflächen bereitstellen ... 336 10.7 ... Zusammenfassung ... 337 11. Berechtigungen implementieren ... 339 11.1 ... Berechtigungsobjekte ... 340 11.2 ... Berechtigungsprüfungen für Geschäftsobjekte ... 347 11.3 ... Anwendungsbenutzer*innen Berechtigungen zuweisen ... 357 11.4 ... Technischen Benutzern Berechtigungen zuweisen ... 371 11.5 ... Zugriff auf SAP-Fiori-Anwendungen bereitstellen ... 373 11.6 ... Zusammenfassung ... 379 12. Business Configuration bereitstellen ... 383 12.1 ... Eine Business-Configuration-Anwendung entwickeln ... 384 12.2 ... Customizing Lifecycle Management ... 395 12.3 ... Zusammenfassung ... 403 13. Erweiterung von Geschäftsobjekten ... 405 13.1 ... Erweiterbare Geschäftsobjekte ermitteln ... 406 13.2 ... Geschäftsobjekt erweitern ... 411 13.3 ... Zusammenfassung ... 431 TEIL III. Weiterführende Konzepte ... 433 14. Kommunikation mit externen Systemen ... 435 14.1 ... Kommunikationsmanagement ... 436 14.2 ... Mit APIs arbeiten ... 447 14.3 ... Anbindung an On-Premise-SAP-Systeme ... 458 14.4 ... Mit Events arbeiten ... 473 14.5 ... Zusammenfassung ... 485 15. Kundeneigenen Quellcode migrieren ... 489 15.1 ... Cloud-Readiness-Prüfungen durchführen ... 490 15.2 ... Migration in das ABAP Environment ... 506 15.3 ... Halbautomatische Quellcodeanpassungen ... 522 15.4 ... Zusammenfassung ... 532 16. Software Lifecycle Management ... 535 16.1 ... Git ... 536 16.2 ... Git-fähiges Change and Transport System ... 539 16.3 ... Software Lifecycle Management im SAP BTP, ABAP Environment ... 540 16.4 ... Software Lifecycle Management in SAP S/4HANA ... 552 16.5 ... Zusammenfassung ... 556 17. ABAP Cloud für On-Premise-Systeme anwenden ... 557 17.1 ... Embedded ABAP Environment ... 558 17.2 ... Vorgehen bei der On-Stack-Erweiterung ... 561 17.3 ... Wahl der richtigen Erweiterungsstrategien ... 570 17.4 ... Zusammenfassung ... 575 Anhang ... 577 A ... Eine ABAP-Serviceinstanz anlegen ... 579 B ... Quellen- und Literaturverzeichnis ... 587 Das Autorenteam ... 591 Index ... 593
Neuronale Netze programmieren mit Python
Neuronale Netze stehen im Mittelpunkt, wenn es um Künstliche Intelligenz und Machine Learning geht. Sie revolutionieren Bild- und Spracherkennung, Spiele-KIs und vieles mehr. Zum Glück lassen sich die genialen Ideen dahinter einfach erklären. Um sie zu verstehen und einzusetzen, programmieren Sie verschiedene Netztypen selbst nach! Und zwar in Python, der Hauptsprache der KI-Welt. Sie werden sich dabei mit Mathematik und Programmierung befassen, brauchen aber keine konkreten Vorkenntnisse. Aus dem Inhalt: Die Grundidee hinter Neuronalen NetzenEin einfaches Neuronales Netz aufbauenNeuronale Netze trainierenÜberwachtes und unüberwachtes LernenEinführung in TensorFlowKompaktkurs PythonWichtige mathematische GrundlagenReinforcement LearningVerschiedene Netzarten und ihre AnwendungsbereicheBack PropagationDeep LearningWerkzeuge für Data Scientists Vorwort ... 13 Vorwort zur 2. Auflage ... 16 Vorwort zur 3. Auflage (in Kooperation mit einem Transformer Neural Network) ... 17 1. Einleitung ... 19 1.1 ... Wozu neuronale Netze? ... 19 1.2 ... Über dieses Buch ... 20 1.3 ... Der Inhalt kompakt ... 22 1.4 ... Ist diese Biene eine Königin? ... 25 1.5 ... Ein künstliches neuronales Netz für den Bienenstaat ... 26 1.6 ... Von der Biologie zum künstlichen Neuron ... 32 1.7 ... Einordnung und der Rest ... 36 1.8 ... Zusammenfassung ... 45 1.9 ... Referenzen ... 45 TEIL I. Up and running ... 47 2. Das minimale Starterkit für die Entwicklung von neuronalen Netzen mit Python ... 49 2.1 ... Die technische Entwicklungsumgebung ... 49 2.2 ... Zusammenfassung ... 72 3. Ein einfaches neuronales Netz ... 73 3.1 ... Vorgeschichte ... 73 3.2 ... Her mit dem neuronalen Netz! ... 73 3.3 ... Neuron-Zoom-in ... 77 3.4 ... Stufenfunktion ... 82 3.5 ... Perceptron ... 84 3.6 ... Punkte im Raum -- Vektorrepräsentation ... 85 3.7 ... Horizontal und vertikal -- Spalten- und Zeilenschreibweise ... 92 3.8 ... Die gewichtete Summe ... 95 3.9 ... Schritt für Schritt -- Stufenfunktionen ... 95 3.10 ... Die gewichtete Summe reloaded ... 96 3.11 ... Alles zusammen ... 97 3.12 ... Aufgabe: Roboterschutz ... 100 3.13 ... Zusammenfassung ... 103 3.14 ... Referenzen ... 103 4. Lernen im einfachen Netz ... 105 4.1 ... Vorgeschichte: Man lässt planen ... 105 4.2 ... Lernen im Python-Code ... 106 4.3 ... Perceptron-Lernen ... 107 4.4 ... Trenngerade für einen Lernschritt ... 110 4.5 ... Perceptron-Lernalgorithmus ... 112 4.6 ... Die Trenngeraden bzw. Hyperplanes oder auch Hyperebenen für das Beispiel ... 117 4.7 ... scikit-learn-kompatibler Estimator ... 120 4.8 ... scikit-learn-Perceptron-Estimator ... 127 4.9 ... Adaline ... 129 4.10 ... Zusammenfassung ... 139 4.11 ... Referenzen ... 141 5. Mehrschichtige neuronale Netze ... 143 5.1 ... Ein echtes Problem ... 143 5.2 ... XOR kann man lösen ... 145 5.3 ... Vorbereitungen für den Start ... 150 5.4 ... Der Plan für die Umsetzung ... 152 5.5 ... Das Setup (»class«) ... 153 5.6 ... Die Initialisierung (»__init__«) ... 155 5.7 ... Was für zwischendurch (»print«) ... 158 5.8 ... Die Auswertung (»predict«) ... 159 5.9 ... Die Verwendung ... 161 5.10 ... Zusammenfassung ... 162 6. Lernen im mehrschichtigen Netz ... 163 6.1 ... Wie misst man einen Fehler? ... 163 6.2 ... Gradientenabstieg an einem Beispiel ... 165 6.3 ... Ein Netz aus sigmoiden Neuronen ... 174 6.4 ... Der coole Algorithmus mit Vorwärts-Delta und Rückwärts-Propagation ... 176 6.5 ... Ein fit-Durchlauf ... 189 6.6 ... Zusammenfassung ... 198 6.7 ... Referenz ... 198 7. Beispiele für tiefe neuronale Netze ... 199 7.1 ... Convolutional Neural Networks ... 199 7.2 ... Transformer Neural Networks ... 216 7.3 ... Das Optimierungsverfahren ... 226 7.4 ... Overfitting verhindern ... 228 7.5 ... Zusammenfassung ... 230 7.6 ... Referenzen ... 231 8. Programmierung von Deep Neural Networks mit TensorFlow 2 ... 233 8.1 ... Convolutional Networks zur Handschriftenerkennung ... 233 8.2 ... Transfer Learning mit Convolutional Neural Networks ... 249 8.3 ... Transfer Learning mit Transformer Neural Networks ... 259 8.4 ... Zusammenfassung ... 264 8.5 ... Referenzen ... 265 TEIL II. Deep Dive ... 267 9. Vom Hirn zum Netz ... 269 9.1 ... Ihr Gehirn in Aktion ... 270 9.2 ... Das Nervensystem ... 270 9.3 ... Das Gehirn ... 271 9.4 ... Neuronen und Gliazellen ... 274 9.5 ... Eine Übertragung im Detail ... 276 9.6 ... Darstellung von Zellen und Netzen ... 279 9.7 ... Zusammenfassung ... 280 9.8 ... Referenzen ... 281 10. Die Evolution der künstlichen neuronalen Netze ... 283 10.1 ... Die 1940er-Jahre ... 284 10.2 ... Die 1950er-Jahre ... 286 10.3 ... Die 1960er-Jahre ... 288 10.4 ... Die 1970er-Jahre ... 288 10.5 ... Die 1980er-Jahre ... 289 10.6 ... Die 1990er-Jahre ... 303 10.7 ... Die 2000er-Jahre ... 303 10.8 ... Die 2010er-Jahre ... 304 10.9 ... Zusammenfassung ... 307 10.10 ... Referenzen ... 307 11. Der Machine-Learning-Prozess ... 309 11.1 ... Das CRISP-DM-Modell ... 309 11.2 ... Ethische und rechtliche Aspekte ... 313 11.3 ... Feature Engineering ... 324 11.4 ... Zusammenfassung ... 353 11.5 ... Referenzen ... 354 12. Lernverfahren ... 355 12.1 ... Lernstrategien ... 355 12.2 ... Werkzeuge ... 384 12.3 ... Zusammenfassung ... 389 12.4 ... Referenzen ... 390 13. Anwendungsbereiche und Praxisbeispiele ... 391 13.1 ... Warmup ... 391 13.2 ... Bildklassifikation ... 394 13.3 ... Erträumte Bilder ... 415 13.4 ... Deployment mit vortrainierten Netzen ... 426 13.5 ... Zusammenfassung ... 430 13.6 ... Referenzen ... 431 Anhang ... 433 A ... Python kompakt ... 435 B ... Mathematik kompakt ... 465 C ... TensorFlow 2 und Keras ... 485 Index ... 497
Julia Quick Syntax Reference
Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia’s APIs, libraries, and packages.This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.WHAT YOU WILL LEARN* Work with Julia types and the different containers for rapid development* Use vectorized, classical loop-based code, logical operators, and blocks* Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts* Build custom structures in Julia* Use C/C++, Python or R libraries in Julia and embed Julia in other code.* Optimize performance with GPU programming, profiling and more.* Manage, prepare, analyse and visualise your data with DataFrames and Plots* Implement complete ML workflows with BetaML, from data coding to model evaluation, and more.WHO THIS BOOK IS FORExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.Antonello Lobianco, PhD is a research engineer employed by a French Grande É cole (polytechnic university). He works on the biophysical and economic modelling of the forest sector and is responsible for the lab models portfolio. He does programming in C++, Perl, PHP, Visual Basic, Python, and Julia. He teaches environmental and forest economics at undergraduate and graduate levels and modelling at PhD level. For a few years, he has followed the development of Julia as it fits his modelling needs. He is the author of a few Julia packages, particularly on data analysis and machine learning (search sylvaticus on GitHub).Part 1. Language Core.- 1. Getting Started.- 2. Data Types and Structures.- 3. Control Flow and Functions.- 4. Custom Types.- E1: Shelling Segregation Model - 5. Input – Output.- 6. Metaprogramming and Macros.- 7. Interfacing Julia with Other Languages.- 8. Efficiently Write Efficient Code. - 9 Parallel Computing in Julia - Part 2. Packages Ecosystem.- 10. Working with Data.- 11. Scientific Libraries.- E2: Fitting a forest growth model - 12 – AI with Julia – E3. Predict house values - 13. Utilities. Appendix: Solutions to the exercises.
Neural Networks with TensorFlow and Keras
Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn * Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs * Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples * Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models * Apply machine learning and neural network techniques in various professional scenarios Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn * Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs * Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples * Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models * Apply machine learning and neural network techniques in various professional scenarios Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques Chapter 1: Introduction to Neural Networks.- Chapter 2: Using Tensors.- Chapter 3: How Machines Learn.- Chapter 4: Network Layers.- Chapter 5: The Training Process.- Chapter 6: Generative Models.- Chapter 7: Re-enforcement Learning.- Chapter 8: Using Pre-trained Networks. Philip Hua brings over 30 years of experience in investment, risk management, and IT. He has held senior positions as a partner at a hedge fund, led risk and IT departments at both large and boutique firms, and co-founded a successful fintech company. Alongside Dr. Paul Wilmott, he developed the CrashMetrics methodology, a crucial tool for evaluating severe market risk in portfolios. Philip holds a PhD in Applied Mathematics from Imperial College London, an MBA, and a BSc in Engineering.
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries._Developing LangChain-based Generative AI LLM Apps with Python_ employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.WHAT YOU WILL LEARN* Understand different types of LLMs and how to select the right ones for responsible AI.* Structure effective prompts.* Master LangChain concepts, such as chains, models, memory, and agents.* Apply embeddings effectively for search, content comparison, and understanding similarity.* Setup and integrate Pinecone vector database for indexing, structuring data, and search.* Build Q & A applications for multiple doc formats.* Develop multi-step AI workflow apps using LangChain agents.WHO THIS BOOK IS FORPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.RABI JAY has over 15 years of experience driving digital transformation with a unique blend of technical depth and business acumen. His background as a Java and SAP ABAP developer provides insights into the enterprise systems LLMs often needed to integrate with. As a leader in Deloitte’s Digital / Cloud Native practice, he has gained cross-industry experience applying AI solutions, positioning him to identify where LLMs offer the greatest potential for business impact.He is passionate about making complex technology accessible, leading him to authoring books on SAP NetWeaver Portal Technology and "Enterprise AI in the Cloud" along with regular contributions to industry publications. His role as a technical reviewer for Large Language Model Based Solutions, Modern Python Development Using ChatGPT, and as Vice President at HCL America, focused on digital transformation, demonstrate his active engagement in the LLM field. Additionally, he runs a LinkedIn newsletter ("Enterprise AI Transformation") and free LinkedIn course (“Generative AI for Business Innovation”).Chapter 1: Introduction to LangChain and LLMs.- Chapter 2: Integrating LLM APIs with LangChain.- Chapter 3: Building Q&A and Chatbot Apps.- Chapter 4: Exploring LLMs.- Chapter 5: Mastering Prompts for Creative Content.- Chapter 6: Building Chatbots and Automated Analysis Systems Using Chains.- Chapter 7: Building Advanced Q&A and Search applications Using Retrieval-Augmented Generation (RAG).- Chapter 8: Your First Agent App.- Chapter 9: Building Different Types of Agents.- Chapter 10: Projects: Building Agent Apps for Common Use Cases. - Chapter 11: Building & Deploying a ChatGPT Like App Using Streamlit.
Hands-on Deep Learning
This book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios.The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT.By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems.WHAT YOU WILL LEARN* What are deep neural networks?* What is transfer learning, multi-task learning, and end-to-end learning?* What are hyperparameters, bias, variance, and data division?* What are CNN and RNN?WHO THIS BOOK IS FORMachine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learningHARSH BHASIN is a researcher and practitioner. He has completed his PhD in Diagnosis and Conversion Prediction of Mild Cognitive Impairment Using Machine Learning from Jawaharlal Nehru University, New Delhi. He worked as a Deep Learning consultant for various firms and taught at various Universities, including Jamia Hamdard, and DTU. He is currently associated with Bennett University.Harsh has authored 11 books, including _Programming in C#_ and _Algorithms._ He has authored more than 40 papers that have been published in international conferences and renowned journals, including Alzheimer’s and Dementia, Soft Computing, Springer, BMC Medical Informatics & Decision Making, AI & Society, etc. He is the reviewer of a few renowned journals and has been the editor of a few special issues. He has been a recipient of Visvesvaraya Fellowship, Ministry of Electronics and Information Technology.His areas of expertise include Deep Learning, Algorithms and Medical Imaging. Apart from his professional endeavours, he is deeply interested in Hindi Poetry: the progressive era and Hindustani Classical Music: percussion instruments.Chapter 1: Revisiting Machine Learning.- Chapter 2: Introduction to Deep Learning.- Chapter 3: Neural Networks.- Chapter 4: Training Deep Networks.- Chapter 5: Hyperparameter Tuning.- Chapter 6: Convolutional Neural Networks: Part 1.- Chapter 7: Convolutional Neural Networks : Part 2.- Chapter 8: Transfer Learning.- Chapter 9: Recurrent Neural Networks.- Chapter 10: LSTM and GRU.- Chapter 11: Autoencoders.- Chapter 12: Introduction to Generative Models.- Appendices A-G.
Java 23 for Absolute Beginners
Write your first code in Java 23 using simple, step-by-step examples that model real-word objects and events, making learning easy. With this book you will be able to pick up core programming concepts without fuss and write efficient Java code in no time. Clear code descriptions and layout ensure you get your code running as soon as possible. Author Iuliana Cosmina focuses on practical knowledge and getting you up to speed quickly—all the bits and pieces a novice needs to get started programming in Java.In this edition, you will discover how Java has changed since version 17, and how to design and write code using the most recently introduced Java features such as new collection methods, virtual threads, pattern and record matching in switch expressions, structured concurrency tasks, unnamed classes and instance methods, and many more.This book is a complete Java guide, covering the following topics: setting up a development environment, programming concepts and well-known programming principles, writing Java code following industry-specific design patterns and coding conventions, executing it, debugging, testing, documenting it and even using specialized tools such as IntelliJ IDEA for writing Java code, Maven for building, JUnit Jupiter for testing, and in-memory and Docker-hosted databases or data storage. After reading this book, you’ll have all the necessary skills and knowledge to pass an interview for a starting Java development position.WHAT YOU WILL LEARN* Set up a Java development environment* Use the Java language to write high-quality code* Understand fundamental programming concepts and algorithms* Use virtual threads, records, and other Java renown features* Debug, test, and document Java code* Improve performance by customizing the Garbage CollectorWHO THIS BOOK IS FORThose who are new to programming and want to learn Java and use it to build efficient solutionsIULIANA COSMINA is currently a software engineer for Cloudsoft, Edinburgh. She has been writing Java code since 2002 and contributed to various types of applications such as experimental search engines, ERPs, track and trace, and banking. During her career, she has been a teacher, a team leader, software architect, DevOps professional, and software manager. She is a Spring-certified Professional, as defined by Pivotal, the makers of Spring Framework, Boot, and other tools, and considers Spring the best Java framework to work with. When she is not programming, she spends her time reading, blogging, learning to play piano, travelling, hiking, or biking.1. An Introduction to Java and its History.- 2. Preparing your Development Environment.- 3. Getting Your Feet Wet.- 4. Java Syntax.- 5. Data Types.- 6. Operators.- 7. Controlling the Flow.- 8. The Stream API.- 9. Debugging, Testing, and Documenting.- 10. Making Your Application Interactive.- 11. Working With Files.- 12. The Publish-Subscribe Framework.- 13. Garbage Collection.- Appendix A.
Programmieren mit JavaScript
JavaScript hat sich mittlerweile von einer, am Anfang teils nicht ganz ernst genommenen, Skriptsprache im Browser zu einer der wichtigsten Programmiersprachen überhaupt entwickelt. Im Kern immer noch als die Sprache im Webbrowser schlechthin populär, erobert JavaScript nach und nach immer mehr Anwendungsgebiete. Bei der clientseitigen Webentwicklung gibt es im Grunde keine Alternative zu JavaScript für die Entwicklung interaktiver und dynamischer Webanwendungen. Nahezu alle Frameworks basieren zudem auf JavaScript. Aber auch zur Unterstützung vieler weiterer Arten von Software kann man JavaScript einsetzen bzw. gibt es auf JavaScript aufsetzende Frameworks.Insbesondere boomt aber die serverseitige Entwicklung mit JavaScript. Beispielsweise mit Node.js können Entwickler JavaScript auf Serverseite ausführen, was die Entwicklung von skalierbaren und leistungsstarken Webanwendungen ermöglicht. Node.js wird zudem häufig für die Erstellung von APIs, Echtzeit-Anwendungen und Microservices verwendet.Entdecken Sie in diesem Buch nun die mittlerweile fast grenzenlosen Möglichkeiten von JavaScript. Von den Grundlagen bis hin zu fortgeschrittenen Techniken werden alle Aspekte der Sprache abgedeckt. Tauchen Sie ein in die Welt von JavaScript und erweitern Sie Ihr Wissen mit praxisnahen Beispielen und bewährten Methoden. Egal, ob Sie mit JavaScript beginnen und die Grundlagen erlernen möchten, oder als erfahrene(r) Entwicklerin bzw. Entwickler das Können vertiefen wollen. Hier finden Sie das Wissen und die Werkzeuge, um Ihre JavaScript-Fähigkeiten auf das nächste Level zu bringen. Einleitung. Erste Beispiele – Der Sprung ins kalte Wasser. Versionen von JavaScript und Einbindung in Webseiten. Elementare JavaScript-Grundstrukturen. Kontrollflussanweisungen. Arrays, JSON und andere iterierbare Elemente. Funktionen, Prozeduren und Methoden. Module. Objekte und JavaScript. Eingebaute Objektdeklarationen. Der DOM – das Mysterium der JavaScript-Welt. Ausnahmebehandlung. Asynchrone Programmierung. Erweiterte Techniken. Eine Frage der Qualität. RALPH STEYER wurde in Wiesbaden geboren und hat nach dem Abitur in Frankfurt Mathematik studiert. Seit 1996 arbeitet er selbstständig als Diplom-Mathematiker im Bereich EDV-Schulung, Programmierung und Beratung. Seine Spezialgebiete sind Internetprogrammierung und objektorientierte Software-Entwicklung. Unter anderem unterrichtet er an Hochschulen sowie für verschiedene Schulungsanbieter. Ralph Steyer ist zudem Autor zahlreicher Fachbücher und Videotrainings zu verschiedenen Programmierthemen.
Spiele programmieren mit Scratch
* 12 COOLE COMPUTERSPIELE GANZ EINFACH MIT SCRATCH ERSTELLEN* VON DEN GRUNDLAGEN BIS ZU CLEVEREN TRICKS WIE BERÜHRUNGEN ERKENNEN UND PUNKTE ZÄHLEN * FÜR KINDER AB 9 JAHREN OHNE VORKENNTNISSE* AKTUELL ZU SCRATCH 3.0Mit diesem Buch lernen Kinder ganz einfach und spielerisch das Programmieren. Hierzu ist nichts weiter nötig als ein PC, die kostenlose Programmiersprache Scratch und dieses Buch.Scratch ist eine visuelle Programmiersprache und damit besonders gut für Kinder und die ersten Gehversuche in der Welt der Programmierung geeignet. Das Buch enthält eine kurze Einführung in die Verwendung von Scratch sowie Anleitungen für 12 spannende Spiele, die Kinder ganz leicht selbstständig umsetzen können. Durch die intuitive Bedienung von Scratch können die Spiele einfach angepasst werden, um der eigenen Kreativität freien Lauf zu lassen.Während sie die Spiele aus dem Buch umsetzen, lernen Kinder spielerisch wichtige Grundlagen der Programmierung wie z.B. die Funktionsweise von Schleifen, Variablen und Funktionen, die später auf andere Programmiersprachen übertragen werden können.Für das Programmieren kleiner Spiele unterwegs enthält das Buch zusätzlich eine kurze Einführung in die Smartphone-App ScratchJr.SPIELE IM BUCH:* Zahlenraten* Versteckspiel* Baseball* Asteroiden abschießen* Tennis* Adventure: Verwunschenes Haus* und viele mehrThomas Kaffka arbeitet als Dozent für IT an einem Gymnasium und in zwei Bildungseinrichtungen, die Schülerinnen und Schülern die IT näherbringen. Er war viele Jahre als Softwareingenieur und Projektleiter in Softwarehäusern sowie Wirtschaftsprüfungs- und Beratungsgesellschaften tätig.
Learning Algorithms for Internet of Things
The advent of Internet of Things (IoT) has paved the way for sensing the environment and smartly responding. This can be further improved by enabling intelligence to the system with the support of machine learning and deep learning techniques. This book describes learning algorithms that can be applied to IoT-based, real-time applications and improve the utilization of data collected and the overall performance of the system.Many societal challenges and problems can be resolved using a better amalgamation of IoT and learning algorithms. “Smartness” is the buzzword that is realized only with the help of learning algorithms. In addition, it supports researchers with code snippets that focus on the implementation and performance of learning algorithms on IoT based applications such as healthcare, agriculture, transportation, etc. These snippets include Python packages such as Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and more._Learning Algorithms for Internet of Things _provides you with an easier way to understand the purpose and application of learning algorithms on IoT.WHAT YOU’LL LEARN* Supervised algorithms such as Regression and Classification.* Unsupervised algorithms, like K-means clustering, KNN, hierarchical clustering, principal component analysis, and more.* Artificial neural networks for IoT (architecture, feedback, feed-forward, unsupervised).* Convolutional neural networks for IoT (general, LeNet, AlexNet, VGGNet, GoogLeNet, etc.).* Optimization methods, such as gradient descent, stochastic gradient descent, Adagrad, AdaDelta, and IoT optimization.WHO THIS BOOK IS FORStudents interested in learning algorithms and their implementations, as well as researchers in IoT looking to extend their work with learning algorithmsDR. G. R. KANAGACHIDAMBARESAN is a Professor in the Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. His main research interests includes IoT, Expert systems and Sensors. He has published several reputed articles and undertaken several consultancy activities for leading MNC companies. He has also guest edited several special issue volumes and books at Springer and serves on the editorial review board for peer reviewed journals.He is currently working on several government-sponsored research projects like ISRO, DBT and DST. He is a TEC committee member in DBT. He is an ASEM-DUO Fellow and has successfully edited several books in EAI Springer. He is currently the Editor-in-Chief for the Next Generation Computer and Communication Engineering Series (Wiley). He received his B.E degree in Electrical and Electronics Engineering from Anna University in 2010 and M.E. Pervasive Computing Technologies in Anna University in 2012. He has completed his Ph.D. in Anna University Chennai in 2017.DR. N. BHARATHI is an Associate Professor in the Computer Science Engineering Department at SRM Institute of Science and Technology in Chennai, India. Her past experiences are Associate Professor at Saveetha School of Engineering, R&D head at Yalamanchili Manufacturing Private Limited, and Assistant professor in SASTRA deemed university. She has good knowledge to work with IoT and embedded system in addition to computer science engineering concepts.She was awarded with a Ph.D. degree in Computer Science in 2014 from SASTRA Deemed University, with 19+ years of work experience as an academic and industrial experience as R&D head involved in ARM platform boards with software development in Ubuntu OS. She completed her M. Tech in Advanced computing in SASTRA deemed University and done her M.Tech project internship at Center for High Performance Embedded System (CHiPES), Nanyang Technological University (NTU), Singapore. She was completed her B.E. in computer science engineering in 2002 at Shanmugha College of Engineering (Bharathidasan University). She published many research papers in reputed journals and conferences along with book chapters, advised many B.Tech. and M.Tech. students in various domains of computer science engineering and embedded systems and is currently advising four research scholars.Chapter 1: Learning Algorithms for IoT.- Chapter 2: Python Packages for Learning Algorithms.- Chapter 3: Supervised Algorithms.- Chapter 4: Unsupervised Algorithms.- Chapter 5: Reinforcement Algorithms.- Chapter 6: Artificial Neural Networks for IoT.- Chapter 7: Convolutional Neural Networks for IoT.- Chapter 8: LSTM, GAN, and RNN.- Chapter 9: Optimization Methods.
Building Scalable Deep Learning Pipelines on AWS
This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies—such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3—to streamline the development, training, and deployment of deep learning models.Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale.By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today’s data-driven landscape.WHAT YOU WILL LEARN* Maximize AWS services for scalable and high-performance deep learning architectures* Harness the capacity of PyTorch and TensorFlow for advanced neural network development* Utilize PySpark for efficient distributed data processing on AWS* Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deploymentWHO THIS BOOK IS FORData scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantageABDELAZIZ TESTAS, PHD, is a seasoned data scientist with over a decade of experience in data analysis and machine learning. He earned his PhD in Economics from the University of Leeds in England and holds a master’s degree in the same field from the University of Glasgow in Scotland. Additionally, he has earned several certifications in computer science and data science in the United States.For over 10 years, Abdelaziz served as a Lead Data Scientist at Nielsen, where he played a pivotal role in enhancing the company’s audience measurement capabilities. He was instrumental in planning, initiating, and executing end-to-end data science projects and developing methodologies that advanced Nielsen’s digital ad and content rating products. His expertise in media measurement and data science drove the creation of innovative solutions.Recently, Abdelaziz transitioned to the public sector, joining the State of California's Department of Health Care Access and Information (HCAI). In his new role, he leverages his coding and data science leadership skills to make a meaningful impact, supporting HCAI’s mission to ensure quality, equitable, and affordable health care for all Californians.Abdelaziz is also the author of Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn (Apress).Chapter 1: Overview of Scalable Deep Learning Pipelines on AWS.- Chapter 2: Setting Up a Deep Learning Environment on AWS.- Chapter 3: Data Preparation with PySpark for Deep Learning.- Chapter 4: Deep Learning with PyTorch for Regression.- Chapter 5: Deep Learning with TensorFlow for Regression.- Chapter 6: Deep Learning with PyTorch for Classification.- Chapter 7: Deep Learning with TensorFlow for Classification.- Chapter 8: Scalable Deep Learning Pipelines with Apache Airflow.- Chapter 9: Techniques for Improving Model Performance.- Chapter 10: Deploying and Monitoring Deep Learning Models.
Java For Dummies
LEARN TO CODE WITH JAVA AND OPEN THE GATE TO A REWARDING CAREERNow in its 9th edition, Java For Dummies gives you the essential tools you need to understand the programming language that 17 million software developers rely on. This beginner-friendly guide simplifies every step of the learning process. You'll learn the basics of Java and jump into writing your own programs. Along the way, you'll gain the skills you need to reuse existing code, create new objects, troubleshoot when things go wrong, and build working programs from the ground up. Java For Dummies will help you become a Java developer, even if you're brand new to the world of coding.* Learn the basic syntax and building blocks of Java* Begin to write your own programs in the latest Java version* Test out your code and problem-solve any errors you find* Discover techniques for writing code fasterThis is the must-have Dummies resource for beginning programmers and students who need a step-by-step guide to getting started with Java. You'll also love this book if you're a seasoned programmer adding another language to your repertoire. DR. BARRY BURD is a professor of Mathematics and Computer Science at Drew University in Madison, NJ. He’s a co-leader of the Garden State Java User Group and New York JavaSIG. In 2020, he was honored to be named a Java Champion. He’s the author of Beginning Programming with Java For Dummies and Flutter For Dummies.
Natural Language Processing on Oracle Cloud Infrastructure
This book demonstrates how to use Oracle Cloud Infrastructure (OCI) and Hugging Face technologies to develop advanced NLP solutions. Through a practical case study, it addresses common NLP challenges and offers strategies for creating efficient, cost-effective transformer-based models. By the end of this book, you will have the skills and knowledge to create cutting-edge NLP solutions on OCI, customized to meet the needs of various industries and projects.The book takes you through the complete NLP solution life cycle—covering data preparation, model fine-tuning, deployment, and monitoring—while highlighting key topics such as cost-effectiveness and responsible AI for NLP implementations. Drawing from real-world experience and offering practical insights, it bridges the gap between theory and practice, equipping you to design and deploy scalable, cost-efficient NLP solutions.WHAT YOU WILL LEARN* Master key NLP concepts and the OCI ecosystem* Create high-quality datasets using Hugging Face and OCI Data Labeling Service* Fine-tune domain-specific pre-trained models from Hugging Face using OCI Data Science Notebook Sessions* Deploy and operationalize your models with OCI Data Science Model Deployments* Automate the NLP life cycle with OCI Data Science Pipelines* Implement cost-effective strategies throughout the entire NLP life cycle, from dataset preparation to model training and deploymentWHO THIS BOOK IS FORA diverse audience interested in implementing NLP solutions on Oracle Cloud Infrastructure: NLP practitioners, data scientists, and machine learning engineers who want to learn how to leverage Oracle AI and Hugging Face to implement an end-to-end NLP solution life cycle, from data preparation to model deployment; Oracle practitioners who want to expand their Oracle expertise by exploring OCI's advanced capabilities for building and scaling cutting-edge NLP solutions in enterprise environments; business decision makers who want to discover the strategic benefits of NLP solutions on OCI, including cost-effectiveness and responsible AI, while driving business valueHICHAM ASSOUDI is an accomplished IT professional and AI expert with over 30 years of experience, including more than 25 years specializing in Oracle technologies. He holds a PhD in Computer Science and is also an OCI Certified Architect. Hicham has held key roles such as Technology Manager at Oracle and IT Architect at IBM, offering highly specialized technical consulting to major corporations across Canada, the US, and Europe.Hicham’s journey into AI began over a decade ago during his doctoral studies, where he initially focused on intelligent agents and general machine learning. He later specialized in natural language processing (NLP) from the early days of transformer models, positioning him at the forefront of NLP innovation. As the founder of Typica.ai, an AI startup, Hicham applies cutting-edge research to practical NLP solutions, empowering organizations to leverage NLP for significant business impact.In addition to his industry contributions, Hicham maintains strong ties to academia as an External Research Associate at the AI Lab of UQAM University in Montreal, bridging the gap between academic research and real-world applications.Part 1: Foundations and Case Study Introduction.- Chapter 1: NLP Essentials.- Chapter 2: Oracle Cloud for NLP.- Chapter 3: Healthcare NLP Case Study.- Part2: Case Study Implementation.- Chapter 4: Tenancy Preparation.- Chapter 5: Dataset Preparation.- Chapter 6: Model Fine-tuning.- Part 3: Case Study Deployment and Wrap-Up.- Chapter 7: Model Deployment and Monitoring.- Chapter 8: MLOps and Conclusion.
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs)
Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. You will: * Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. * Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? * Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. * Analyze the impact of a machine learning-based solution on a specific UN SDG. Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. What You’ll Learn * Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. * Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? * Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. * Analyze the impact of a machine learning-based solution on a specific UN SDG. Who This Book Is For Data scientists, machine learning engineers, software professionals, researchers, and graduate students. Dr. Tulsi Pawan Fowdur received his BEng (Hons) degree in Electronic and Communication Engineering with honors from the University of Mauritius in 2004. He was also the recipient of a Gold medal for having produced the best degree project at the Faculty of Engineering in 2004. In 2005 he obtained a full-time PhD scholarship from the Tertiary Education Commission of Mauritius and was awarded his PhD degree in Electrical and Electronic Engineering in 2010 by the University of Mauritius. He is also a Registered Chartered Engineer of the Engineering Council of the UK, Fellow of the Institute of Telecommunications Professionals of the UK, and a Senior Member of the IEEE. He joined the University of Mauritius as an academic in June 2009 and is presently an Associate Professor at the Department of Electrical and Electronic Engineering of the University of Mauritius. His research interests include Mobile and Wireless Communications, Multimedia Communications, Networking and Security, Telecommunications Applications Development, the Internet of Things, and AI. He has published several papers in these areas and is actively involved in research supervision, reviewing papers, and also organizing international conferences. Lavesh Babooram received his BEng (Hons) degree in Telecommunications Engineering with Networking with honors from the University of Mauritius in 2021. He was also awarded a Gold medal for having produced the best degree project at the Faculty of Engineering in 2021. Since 2022, he has been an MSc by Applied Research student at the University of Mauritius. With in-depth knowledge of telecommunications applications design, analytics, and network infrastructure, he aims to pursue research in Networking, Multimedia Communications, Internet of Things, Artificial Intelligence, and Mobile and Wireless Communications. He joined Mauritius Telecom in 2022 and is currently working in the Customer Experience and Service Department as a Pre-Registration Trainee Engineer. Chapter 1: Introduction to Machine Learning Applications Development and the UN SDGs.- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms.- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12.- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms.- Chapter 5: Water Potability Testing Using Machine Learning.- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13.- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11.
Architecting Enterprise AI Applications
This book explores how to define, design, and maintain enterprise AI applications, exploring the impacts they will have on the teams who work with them.The book is structured into four parts. In Part 1: Defining Your AI Application, you are introduced to the dynamic interplay between human adaptability and AI specialization, the concept of meta systems, and the mechanics of prediction machines. In Part 2: Designing Your AI Application, the book delves into the anatomy of an AI application, unraveling the intricate relationships among data, machine learning, and reasoners. This section introduces the building blocks and enterprise architectural framework for designing multi-agent systems. Part 3: Maintaining Your AI Application takes a closer look at the ongoing life cycle of AI systems. You are guided through the crucial aspects of testing and test automation, providing a solid foundation for effective development practices. This section covers the critical tasks of security and information curation that ensure the long-term success of enterprise AI applications. The concluding section, Part 4: AI Enabled Teams, navigates the evolving landscape of collaborative efforts between humans and AI. It explores the impact of AI on remote work dynamics and introduces the new roles of the expert persona and the AI handler. This section concludes with a deep dive into the legal and ethical dimensions that AI-enabled teams must navigate.This book is a comprehensive guide that not only equips developers, architects, and product owners with the technical know-how of AI application development, but also delves into the broader implications for teams and society.WHAT YOU WILL LEARN* Understand the algorithms and processes that enable AI to make accurate predictions and enhance decision making* Grasp the concept of metasystems and their role in the design phase of AI applications* Know how data, machine learning, and reasoners drive the functionality and decision-making capabilities of AI applications* Know the architectural components necessary for scalable and maintainable multi-agent AI applications* Understand methodologies for testing AI applications, ensuring their robustness, accuracy, and reliability in real-world applications* Understand the evolving dynamics of human-AI coordination facing teams in the new enterprise working environmentWHO THIS BOOK IS FORA diverse audience, primarily targeting enterprise architects, middle managers, tech leads, and team leads entrenched in the IT sector or possessing a tech-savvy background, including professionals such as digital marketers. Additionally, tech-savvy individual contributors—ranging from digital content creators and data analysts to administrators and programmers—stand to benefit significantly.ANTON CAGLE is a seasoned leader specializing in cloud automation and AI Ops, boasting over two decades of expertise in enterprise architecture and application design. With a passion for delivering democratized, data-driven solutions and automation, Anton focuses on empowering medium to large-sized companies. His dedication extends to mentoring and coaching engineers at all skill levels, fostering a culture of continuous learning and innovation.Recognizing the pivotal role of cloud, data, and AI in shaping the future of business software, Anton is on a mission to guide companies beyond basic automation solutions. His goal is to seamlessly integrate big data and machine learning into organizational frameworks, preparing businesses for the next wave of scalable operations. Anton's approach has led to remarkable transformations for clients, including the reduction of deployment process waste, accelerated feature time to market, and the implementation of cutting-edge cloud data architectures.AHMED CEIFELNASR AHMED is a highly skilled ML engineer, data scientist, and cloud engineer with over six years of experience in developing and deploying data-driven solutions. Ahmed specializes in building and fine-tuning machine learning models, leveraging advanced deep learning techniques, and optimizing cloud-based solutions. His expertise extends to cloud engineering and DevOps practices, where he excels in designing and implementing scalable, efficient cloud architectures and automating deployment processes.With hands-on experience in AWS Cloud environments and a strong background in cloud tools, Ahmed is adept at integrating AI with cloud technologies to create robust, production-ready solutions. He has a proven track record of driving impactful results across various industries, from retail and real estate to fitness and enterprise applications.Ahmed is committed to continuous learning and growth, always seeking to make a significant impact in the fields of AI, data science, and cloud engineering. His career reflects his dedication to advancing technology, optimizing cloud infrastructure, and fostering innovation through data-driven strategies and cutting-edge technology.Part 1: Defining Your AI Application.- Chapter 1: Human Flexibility and AI Specialization.- Chapter 2: Meta Systems.- Chapter 3: Prediction Machines.- Part 2: Designing Your AI Application.- Chapter 4: Anatomy of an AI Application.- Chapter 5: Data, Machine Learning, and Reasoners.- Chapter 6: Large Language Models (LLMs).- Chapter 7: AI Agents.- Part 3: Maintaining Your AI Application.- Chapter 8: Testing Your Enterprise AI Application.- Chapter 9: Testing automation for enterprise ai applications.- Chapter 10: Security.- Chapter 11: Information Curation.- Part 4: AI Enabled Teams.- Chapter 12: Remote Work and Reskilling.- Chapter 13: Expert Personas.- Chapter 14: The Role of the AI Handler.- Chapter 15: Legal and Ethical Considerations.
SQL Schnelleinstieg
Datenbanken abfragen und verwalten in 14 TagenMit diesem Buch gelingt Ihnen der einfache Einstieg in das Datenbankmanagement mit der Abfragesprache SQL. Alle Grundlagen werden in 14 Kapiteln leicht verständlich anhand von Beispielen erläutert.Der Autor führt Sie Schritt für Schritt in den Einsatz von SQL und die Grundlagen relationaler Datenbanken ein: von der fundamentalen Syntax und den ersten Datenbankabfragen über das Verknüpfen von Tabellen bis hin zur Datenbankerstellung und der Zugriffskontrolle.Alle Beispiele beziehen sich auf eine Beispieldatenbank, die auf der Webseite des Verlags zum Download bereitsteht, und lassen sich leicht auf eigene Anwendungsfälle übertragen. Die Besonderheiten bei der Verwendung von PostgreSQL und MySQL werden bei jedem Beispiel beschrieben.So sind Sie perfekt auf den Einsatz von SQL im professionellen Umfeld vorbereitet.Aus dem Inhalt:Alle Grundlagen einfach erläutertSQL-Syntax und Datenbank-AbfragenFiltern mit BedingungenGruppierung von Daten und AggregatfunktionenVerknüpfen von Tabellen und ErgebnissenSkalarfunktionen und UnterabfragenDatenbanken erstellen und anpassenRechteverwaltung und ZugriffskontrolleKomplexe SQL-Konzepte – Trigger, Indizes und ViewsLeseprobe (PDF-Link)Über den Autor:Erik Schönwälder arbeitet als wissenschaftlicher Mitarbeiter (Doktorand) an der Technischen Universität Dresden am Lehrstuhl für Datenbanken. Als Dozent lehrt er hauptsächlich Themen wie Datenbank-Engineering, Information Retrieval, SQL und NoSQL. Auch außerhalb der Universität unterrichtet er seine Fachgebiete online, beispielsweise bei der Heise Academy oder auf Udemy.
Erstellung hochwertiger Shader für Unity®
Shader gelten oft als mystisch und schwer zu entwickeln, selbst für erfahrene Programmierer, Künstler und Entwickler aus anderen Bereichen des Spieldesigns. Dieses Buch räumt mit dieser Vorstellung auf, indem es Ihr Wissen über in Stufen aufbaut. Es beginnt mit grundlegenden Shader-Mathematiken, erklärt, wie sich die Denkweise der Shader-Entwicklung von anderen Arten von Kunst und Programmierung unterscheidet, und geht dann langsam auf Themen wie Vertex- und Fragment-Shader, Beleuchtung, Tiefenbasierte Effekte, Texture Mapping und Shader Graph ein.Jedes dieser Themen wird mit einer umfassenden Aufschlüsselung, der benötigten Theorie und einigen praktischen Anwendungen für die im jeweiligen Kapitel gelernten Techniken präsentiert. Der HLSL (High Level Shading Language)-Code und Shader Graphs werden für jeden relevanten Abschnitt bereitgestellt, ebenso wie zahlreiche Screenshots.Am Ende des Buches werden Sie ein gutes Verständnis für den Shader-Entwicklungsprozess haben und bestens gerüstet sein, um Ihre eigenen ästhetischen und leistungsstarken Shader-Effekte für Ihre Spiele zu erstellen!WAS SIE IN DIESEM BUCH LERNEN Shaders in den Rendering-Pipelines von Unity zu verwenden Shader zu schreiben und ihr Verhalten mit C#-Skripting zu ändern Shader Graph für eine Entwicklung ohne Code zu nutzen Die wichtige Mathematik hinter Shadern zu verstehen, insbesondere Raumtransformationen Die Leistung von Shadern zu analysieren, um Optimierungspotenziale zu identifizieren ZUM INHALT Kapitel 1: Einführung in Shader in Unity. Kapitel 2: Mathematik für die Shader-Entwicklung. Kapitel 3: Ihr allererster Shader. Kapitel 4: Shader-Grafik. Kapitel 5: Texturen, UV-Koordinaten & Normal Mapping. Kapitel 6: Transparenz. Kapitel 7: Der Tiefenpuffer. Kapitel 8: Transparenz und Alpha. Kapitel 9: Weitere Shader-Grundlagen. Kapitel 10: Beleuchtung und Schatten. Kapitel 11: Bildeffekte und Nachbearbeitung. Kapitel 12: Erweiterte Shader. Kapitel 13: Profiling und Optimierung. Kapitel 14: Shader-Rezepte für Ihre Spiele. ZIELGRUPPENDieses Buch richtet sich an Anfänger in der Shader-Entwicklung oder Leser, die den Schritt von Shader-Code zum Shader Graph machen möchten. Es enthält auch einen Abschnitt über Shader-Beispiele für diejenigen, die bereits die Grundlagen von Shadern kennen und nach spezifischen Anwendungsfällen suchen.DANIEL ILETT ist Doktorand an der University of Warwick. Als leidenschaftlicher Spieleentwickler hat er sich auf Shader und technische Kunst spezialisiert. Er ist Autor verschiedener Lehr- und Tutorial-Inhalte, darunter Videos und schriftliche Arbeiten, die sich an Anfänger und fortgeschrittene Entwickler richten. Darüber hinaus arbeitet er freiberuflich an Shadern und visuellen Effekten für Spiele.
Helidon Revealed
This book is your comprehensive introduction to Project Helidon, Oracle’s Java-based microservices framework for building cloud-native applications. The book introduces the two flavors of Helidon—Helidon SE and Helidon MP—and shows how to quickly build applications with each one.The book begins by placing the Helidon framework in context and explaining the MicroProfile and Jakarta EE specifications that underlie the building of microservices-based applications. From there, you will learn about the individual components of Helidon SE and Helidon MP, and you will see examples of building applications for both flavors, and for MicroProfile and Jakarta EE. Finally, the book shows how to convert Helidon applications from Java bytecode into native binaries for instant application startup and peak performance.WHAT YOU WILL LEARN* Choose correctly between Helidon SE and Helidon MP* Create starter applications using the Helidon SE and Helidon MP quick starts* Create server and client applications with Helidon SE components* Create a microservices-based application with the MicroProfile specifications* Create a database application with the Jakarta NoSQL specificationWHO THIS BOOK IS FORJava developers who create microservices applications; those interested in building such applications using the Helidon framework; and developers of microservices applications who want functionality such as native-image support through GraalVM, compatibility with MicroProfile, easy connectivity to database engines, and support for reactive development patternsMICHAEL REDLICH has been an active member within the Java community for the past 25 years. He founded the Garden State Java User Group in 2001 that remains in continuous operation. Since May 2016, Mike has served as a Java community news editor for InfoQ where his contributions include news items, technical articles and technical reviews for external authors. He has been the lead Java Queue Editor since July 2021. Mike was named a Java Champion in April 2023.Mike has presented at venues such as Oracle Code One, JCON, Emerging Technologies for the Enterprise, Trenton Computer Festival (TCF), TCF IT Professional Conference, and numerous Java User Groups. Mike serves as a committer on the Jakarta NoSQL and Jakarta Data specifications, and participates on the leadership council of the Jakarta EE AmbassadorsMike recently retired from ExxonMobil Technology & Engineering in Clinton, New Jersey with 33½ years of service. His experience included developing custom scientific laboratory applications, web applications, chemometrics, polymer physics, infrared spectroscopy and automotive testing. He also has experience as a Technical Support Engineer at Ai-Logix, Inc. (now AudioCodes) where he provided technical support and developed telephony applications for customers.Mike makes his home in Flemington, New Jersey with his lovely wife, Rowena, where they spend quality time cycling and traveling to New Orleans, Louisiana and Newport, Rhode Island throughout the year.PART I. GETTING STARTED.- 1. Introduction to Project Helidon.- 2. The MicroProfile Specification.- 3. The Jakarta EE Specifications.- PART II. HELIDON SE.- 4. Generating a Project with Helidon SE.- 5. Helidon Web Server.- 6. Helidon Web Client.- 7. Helidon Config.- 8. Helidon DB Client.- 9. Helidon Security.- PART III. HELIDON MP.- 10. Generating a Project with Helidon MP.- 11. Helidon Metrics.- 12. Helidon Fault Tolerance.- 13. Helidon Health Checks.- Appendix A. MicroProfile Release History.- Appendix B. The Jakarta EE Specifications.- Appendix C. Helidon Reactive Web Server.
Einstieg in Python (2. Auflg.)
Einstieg in Python - Grundlagen der Python-Programmierung leicht und verständlich erklärt, in 2. Auflage aus Dezember 2024Sie wollen endlich programmieren lernen und ihre ersten Projekte umsetzen. Hierfür bietet sich Python als eine der populärsten Programmiersprachen an. Mit diesem Buch gelingt Ihnen mühelos ein Einstieg, denn Sie werden auf Ihrer Entdeckungsreise vom erfahrenden Trainer Michael Inden begleitet. Er erklärt Ihnen die Grundlagen der Python-Programmierung leicht und verständlich. Generell wird die trockene Theorie auf ein Minimum reduziert. Ergänzend werden immer wieder auch Python-spezifische Besonderheiten wie Built-in-Funktionen, Slicing, Comprehensions, Generatoren usw. erklärt, die das Entwicklen erleichtern und Ihre Programme gleich von Anfang an Python-like, stilistisch schön – kurz Pythonic – machen.Eine große Rolle spielt der interaktive Einstieg mithilfe der Python-Kommandozeile. Damit können kleine Programme direkt ausprobiert werden und Erfolgserlebnisse stellen sich schnell ein. Dieses Vorgehen eignet sich ideal, um sich Python im Selbststudium sowie im eigenen Tempo anzueignen. Allmählich werden die Themen anspruchsvoller und die zu erstellenden Programme größer. Schließlich erfahren Sie, wie Sie eine Entwicklungsumgebung einsetzen, und lernen die objektorientierte Programmierung kennen. Mit den erworbenen Grundlagen können Sie sich immer gezielter mit eigenen Projekten beschäftigen. Erste Ideen liefern drei etwas umfangreichere Programmierbeispiele aus der Praxis, die zudem einen Einblick in das schrittweise, erfolgreiche Entwickeln von Applikationen geben.Das Buch besteht aus in sich abgeschlossenen, aufeinander aufbauenden Kapiteln zu den wesentlichen Bereichen der Programmiersprache Python und den relevanten Sprachelementen. Aufgaben und Musterlösungen runden viele Kapitel ab, sodass Sie das zuvor Gelernte direkt anhand neuer Problemstellungen praktizieren und Ihr Wissen vertiefen können.Zahlreiche kurze Codebeispiele verdeutlichen die Lerninhalte und laden zum Experimentieren ein. Gleich von Anfang an lernen Sie, Ihren Sourcecode sauber zu strukturieren und einen guten Stil zu entwickeln. Dabei hilft ein Kapitel zu Programmierregeln, sogenannten Coding Conventions und zum Testen mit Pytest.Diese zweite Auflage ergänzt Kapitel zur Bildverarbeitung sowie zu populären LLMs und deren vielfältigen Einsatzmöglichkeiten. Schließlich werden die Neuerungen aus Python 3.13 im Anhang vorgestellt.Leseprobe (PDF-Link)
C++ Essentials For Dummies
THE QUICK AND CRYSTAL-CLEAR GUIDE TO C++ PROGRAMMINGC++ Essentials For Dummies is your useful reference to the key concepts of C++, the popular general-purpose language utilized everywhere from building games to writing parts of operating systems. With minimal review and background material—and absolutely no fluff—this book gets straight to the essential topics you need to know to ramp up, brush up, or level up.* Get a helpful intro to the basic concepts of coding in C++* Review what you already know or pick up essential new skills* Create projects that run smoothly with the C++ language* Keep this concise reference book handy for jogging your memory as you workGreat for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, C++ Essentials For Dummies is a fantastic refresher guide that you can always turn to for answers. JOHN PAUL MUELLER produced more than 100 books and more than 600 articles on a range of topics, including functional programming techniques, application development using C++, and machine learning methodologies. RONALD MAK teaches computer science and data science at San Jose State University. He was formerly a senior scientist at NASA and JPL, and has written books on software design, compiler construction, and numerical computing.
Python lernen mit KI-Tools
Nutzen Sie KI-Assistenten, um Python schneller und effektiver zu lernen! Eine der ersten Einführungen in die Programmierung, die virtuelle Assistenten wie ChatGPT (LLMs) aktiv und didaktisch durchdacht für einen besseren Lernerfolg nutzt Die Übungsaufgaben werden mit ChatGPT gelöst und dabei auch der Einsatz von KI-Assistenten als Hilfsmittel für die Programmierung gezeigt Inklusive Prompting, Testen von Code und Debugging mit LLMs Als Lernumgebung werden Jupyter Notebooks genutzt Python eignet sich hervorragend für den Einstieg in die Programmierung. Dieser abwechslungsreiche Praxiskurs führt Sie Schritt für Schritt in die Sprache ein, beginnend mit grundlegenden Konzepten über Funktionen und Datenstrukturen bis hin zur objektorientierten Programmierung. Das Buch geht auch auf die spannenden neuen Möglichkeiten ein, die KI-Assistenten wie ChatGPT Programmiererinnen und Programmierern bieten, und zeigt effektives Prompting, das Testen von Code und das Debugging mit KI. Durch zahlreiche Übungen, bei denen KI-Tools zum Einsatz kommen, sowie Fallstudien, kurze Verständnistests und kleinere Projekte in Jupyter Notebooks können Sie die neu erlernten Programmierkonzepte gleich ausprobieren. Auf diese Weise festigen Sie Ihr Verständnis für die konkrete Programmierpraxis und erfahren, wie KI-Assistenten Sie auch zukünftig im Programmieralltag unterstützen können. Starten Sie mit den Grundlagen, einschließlich Sprachsyntax und Semantik Entwickeln Sie eine klare Vorstellung von den jeweiligen Programmierkonzepten Lernen Sie Variablen, Anweisungen, Funktionen und Datenstrukturen kennen Arbeiten Sie mit Dateien und Datenbanken Verstehen Sie Objekte, Methoden und objektorientierte Programmierung Wenden Sie Debugging-Techniken an, um Syntax-, Laufzeit- und Semantikfehler zu beheben Nutzen Sie KI-Tools von Anfang an, um Ihren Lernprozess zu beschleunigen