ScholarVox Université : Nouveautés : Informatique http://univ.scholarvox.com <![CDATA[ Design with Canva in easy steps ]]> https://univ.scholarvox.com/catalog/book/88984203 Editeur : In Easy Steps

<p>Eye-catching graphics for social media, websites, presentations, and documents used to need a professional designer. Not any more!</p><p>With Canva, anyone can turn ideas into polished designs, and <strong>Design with Canva in easy steps</strong> shows you how. It explains Canva interface and features (online version and the mobile app), shows how to use built-in AI to speed up your work, and covers design principles that make the difference: </p><li>Pairing fonts and using color well</li><li>Headings, white space, and alignment</li><li>Visual hierarchy and logical content flow</li><li>Consistent and impactful branding</li><p>Dedicated chapters on social media, websites, photo and video editing, presentations, documents, and even spreadsheets take you through complete projects from start to finish. </p><p><strong>Take control of all your design needs – for business or for personal projects! </strong></p>

]]> 2026-11-11T08:00:02.283 <![CDATA[ Data Engineering for Cybersecurity : A practical guide to building modern cybersecurity data pipelines (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984489 Editeur : BPB Publications

Description
Cybersecurity today relies on secure, scalable, and intelligent data engineering to combat increasingly sophisticated cyber threats. This book demonstrates how to build cloud-native security data pipelines on Microsoft Azure, equipping cybersecurity, cloud, and data professionals with the skills needed to design resilient, enterprise-ready security solutions.

This book provides a practical, end-to-end guide to building secure, scalable, and intelligent security data pipelines on Microsoft Azure. The book bridges the gap between cybersecurity and data engineering by covering the complete lifecycle of security data—from collection and transformation to storage and future trends. Each chapter combines real-world architectures and best practices to help readers design enterprise-grade cybersecurity solutions.

By the end of this book, you'll have the hands-on expertise and knowledge to design, build, secure, automate, and optimize enterprise-grade cybersecurity data pipelines. You'll be equipped to implement modern security data engineering solutions, improve threat detection and incident response, and confidently support cloud-native security operations in real-world enterprise environments.

What you will learn
? Learn how to build security data pipelines in the cloud.
? Collect security logs from cloud, network, and endpoint systems.
? Transform and enrich security data for better threat analysis.
? Build real-time pipelines for faster threat detection and response.
? Protect security data with encryption and access-control techniques.
? Automate security operations with Azure services and workflows.
? Use machine learning to detect anomalies and security threats.

Who this book is for
This book is written for cybersecurity analysts, cloud engineers, data engineers, DevSecOps professionals, SOC engineers, security architects, and system administrators who want to build modern security data pipelines on Microsoft Azure. It is also valuable for IT professionals and students looking to strengthen their practical skills in cloud security, data engineering, and enterprise cybersecurity.

Table of Contents
1. Introduction to Data Engineering for Cybersecurity
2. Collecting Security Data at Scale
3. Data Transformation and Enrichment for Cybersecurity
4. Building Real-time Security Data Pipelines
5. Security Data Storage and Management
6. Securing Data Pipelines with Encryption and Access Control
7. Version Control and Change Management for Security Data
8. Automating Cybersecurity Data Workflows
9. Advanced Caching and Optimization Techniques
10. Machine Learning for Security Data Engineering
11. Future Trends and Best Practices

]]> 2026-11-11T08:00:02.283 <![CDATA[ AI Systems Development using LLMs : Design and deploy AI applications with LLMs, RAG, and agents (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984496 Editeur : BPB Publications

Description
Large language models have rapidly transformed artificial intelligence, natural language processing, and modern machine learning, making them essential tools for modern software development. This book is your practical roadmap to understanding these technologies and building intelligent, real-world AI applications.

This book systematically covers the essential concepts needed to build, scale, and secure intelligent systems. It begins with the foundations of large language models, scaling laws, ethical considerations, and developer tools like LangChain, LlamaIndex, and Ollama. Next, it guides you through prompt engineering, reasoning techniques, and semantic vector search using custom embeddings. You will then learn how to design advanced retrieval-augmented generation (RAG) systems with vector databases like ChromaDB, FAISS, and Weaviate, alongside conversational memory and evaluation frameworks. Finally, the book covers autonomous AI agent orchestration, multimodal models such as GPT-4o and LLaVA, cloud-to-edge deployment, optimization techniques like LoRA and quantization, prompt security, and emerging standards such as the model context protocol.

By the end of this book, readers will be well-equipped to design, deploy, and secure production-grade AI systems, possessing the practical skills required to solve complex real-world engineering challenges. They will learn to build scalable, secure, and trustworthy LLM applications for academic, industrial, and entrepreneurial settings.

What you will learn
? Master LLM foundations, scaling, and LangChain ecosystem framework tools.
? Orchestrate multi-agent systems with ReAct workflows and safety interlocks.
? Build conversational and RAG applications using vector databases and evaluation frameworks.
? Develop, orchestrate, govern, and secure task-oriented AI agents.
? Deploy and optimize scalable, secure, and multimodal LLM applications.
? Implement semantic chunking across ChromaDB, FAISS, and Weaviate databases.

Who this book is for
This book is intended for undergraduate and postgraduate students, AI/ML engineers, data scientists, software developers, researchers, system architects, cloud engineers, educators, startup teams, and professionals seeking to build practical applications using large language models. Basic familiarity with Python programming and introductory machine learning concepts is recommended.

Table of Contents
1. Introduction to Large Language Models
2. LLM Tools and Frameworks
3. Open-source LLM Ecosystem
4. Ethics, Bias, and Responsible AI
5. Fundamentals of Prompt Engineering
6. Advanced Prompt Engineering Techniques
7. Embeddings and Semantic Search
8. Fine-tuning and Customizing Embeddings
9. Chunking Strategies and Vector Databases
10. Conversational Applications with LLMs
11. Designing Retrieval-augmented Generation Systems
12. Advanced RAG and Explainability
13. Building Foundations of AI Agents
14. Orchestrating and Securing AI Agents
15. Deployment and Optimization of LLM Systems
16. Security and Safety in LLM Applications
17. Multimodal LLMs for Text, Vision, and Speech
18. Future of AI Systems with LLMs

]]> 2026-11-11T08:00:02.283 <![CDATA[ Vision Language Models : Designing multimodal AI systems for real-world enterprise applications (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984503 Editeur : BPB Publications

Description
Enterprise AI is moving beyond text to systems that can understand images, documents, video, and language together. Vision-language models make this possible, but most real-world problems are not solved by models alone. They require well-designed pipelines, data flows, and integration with business systems.

This book focuses on building those systems end-to-end. It covers the foundations of visual and language representations and moves into modern multimodal models. From there, it discusses visual reasoning, visual question answering, and multimodal retrieval. You will work with real code using frameworks such as PyTorch, Hugging Face, OpenCV, YOLO, and learn how to combine vision encoders and retrieval layers into a single workflow. As the book progresses, it focuses on full system design. You will build pipelines that handle data ingestion, pre-processing, model inference, and output generation. The book also covers practical aspects such as model optimization, quantization, and efficient deployment to reduce latency and cost. It explains how to integrate these components into enterprise use cases, evaluate performance, and make systems reliable and scalable.

By the end of this book, you will be able to design and implement production-ready vision-language solutions that solve real business problems.

What you will learn
? Understand core concepts behind vision and language representations.
? Implement custom self-attention and cross-attention in PyTorch.
? Evaluate image captions and VQA benchmarks using CIDEr and CLIPScore.
? Develop visual question answering and multimodal retrieval systems.
? Apply model optimization techniques including quantization for efficiency.
? Design scalable and reliable enterprise-ready AI system architectures.

Who this book is for
This book is intended for machine learning engineers, data scientists, AI engineers, and software developers who want to build multimodal AI systems using vision and language models. It is also relevant for architects and technical leaders designing enterprise AI solutions. Readers are expected to have a basic understanding of Python and machine learning concepts. Familiarity with deep learning frameworks will help in implementing the examples and understanding system design decisions across real-world applications.

Table of Contents
1. Introduction to Vision-language Models
2. Deep Learning and Attention Mechanisms
3. Foundations of Computer Vision for VLMs
4. Architecture of Vision-language Models
5. Dataset Designs, Evaluation Metrics and Benchmarks for VLMs
6. Pneumonia Detection from Chest X-rays
7. Object Detection and Classification with Bounding Boxes
8. Real-World Project 3 Wind Turbine Blade Damage Detection
9. Efficient Tuning, Transfer Learning, Alignment and Optimization
10. Quantization, Distillation and Efficient Inference
11. Vision-language Applications Across Domains
12. Challenges, Ethics and Road Ahead

]]> 2026-11-11T08:00:02.283 <![CDATA[ L'IA sur votre PC : Sans internet et sans abonnement ]]> https://univ.scholarvox.com/catalog/book/88983964 Editeur : AZURATEC

Et si votre ordinateur pouvait vous aider à écrire, à résumer, à comprendre vos chiffres, sans envoyer une seule ligne sur Internet et sans payer d’abonnement ?

C’est possible aujourd’hui, avec un PC ordinaire sous Windows 11, même modeste. Ce guide vous montre comment faire, pas à pas, sans jargon et sans rien supposer.
Vous apprendrez à :
– vérifier ce que supporte votre ordinateur ;
– installateur Ollama et LM Studio, deux logiciels gratuits ;
– analyser un fichier Excel sans confier vos données à personne ;
– choisir et télécharger un modèle d’IA adapté à votre machine ;
– résoudre les problèmes les plus courants.

Chaque étape s’appuie sur des captures d’écran, des astuces, des mises en garde et un petit quiz. Un fichier d’exercice à télécharger vous permet de pratiquer tout de suite.

Pas besoin d’être informaticien : il suffit de savoir installer un programme et de vouloir garder ses données chez soi.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Windows 11 pour les séniors ]]> https://univ.scholarvox.com/catalog/book/88983967 Editeur : AZURATEC

Windows 11 n’a jamais été aussi simple.

Vous avez un ordinateur sous Windows 11 et vous souhaitez l’utiliser sereinement, sans vous sentir dépassé·e ? Ce guide a été conçu spécialement pour vous.

Pas de jargon technique, pas de complications inutiles : uniquement des explications claires, des gestes concrets et des conseils pratiques pour :

  • vous repérer facilement sur votre écran,
  • naviguer sur Internet en toute confiance,
  • envoyer et recevoir des e-mails,
  • rester en contact avec vos proches,
  • ranger et retrouver vos documents et photos,
  • imprimer et sauvegarder vos fichiers,
  • protéger vos mots de passe et éviter les arnaques,
  • résoudre les petits soucis du quotidien.

Avec ce livre, Windows 11 devient un outil utile, accessible et rassurant. À votre rythme, page après page, vous gagnerez en autonomie et en tranquillité.

Le compagnon idéal pour découvrir ou redécouvrir l’informatique... en douceur.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Machine Learning for Time Series with Python : Python to forecast, predict, and monitor time series with modern ML and foundation models at scale ]]> https://univ.scholarvox.com/catalog/book/88984435 Editeur : Packt Publishing

Get better insights from time-series data and become proficient in building models with real-world data

Key Features

  • Explore time series forecasting and time series analysis in Python using ARIMA, SARIMA, GARCH, gradient boosting, and recurrent neural networks.
  • Improve predictive modeling with feature engineering and forecasting machine learning techniques.
  • Apply demand forecasting and financial forecasting methods through practical case studies and real-world datasets.

Book Description

Machine Learning for Time Series, Second Edition offers a practical guide to building forecasting systems with the most important libraries such as sktime, darts, Nixtla's skforecast and mlforecast, and AutoGluon among others. Starting with the fundamentals, you’ll learn how to characterize datasets, measure and validate prediction performance, engineer features, and build forecasting pipelines. The book covers classical methods such as ARIMA and Theta, machine learning methods such as gradient boosting, and deep learning with recurrent, latest Transformer-based (like Crossformer), or convolutional (TimesNet) models. We'll apply foundation models such as TimesFM, Chronos, and OpenTSLM, or generative approaches like Chain-of-Charts, and uncertainty quantification with conformal prediction such as GARCH conformal, conformalized quantile regression, or split conformal. We’ll also dive into production infrastructure like feature stores, model tracking, and monitoring and scaling strategies, as well as classification, self-supervised encoding, spatiotemporal modeling, handling irregular time series with ODEs, survival analysis, and causal inference. Through practical examples, you’ll learn how to evaluate models, detect and handle anomalies and drift, and choose approaches that match data and operational needs.

What you will learn

  • Apply classical time series models such as ETS, ARIMA, and Theta
  • Validate with SMAPE, MASE, RMSSE and rolling window, and walk-forward
  • Forecast as regression with sktime, darts, mlforecast, and skforecast
  • Use modern deep learning like LSTMs, DLinear, TSMixer, and TimesNet
  • Grasp foundation models such as TimesFM, OpenTSLM, or Chain-of-Charts
  • Quantify uncertainty quantification with conformalized regression
  • Set up feature stores, MLflow, and monitoring and scaling strategies
  • Handle classification, drift, spatiotemporal and irregular time series

Who this book is for

This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Modeling Software Architecture : Communicate system structure, behavior, and trade-offs with C4, viewpoints, and perspectives ]]> https://univ.scholarvox.com/catalog/book/88984450 Editeur : Packt Publishing

Model software architecture with C4, connected viewpoints, and perspectives. Create clear architecture diagrams, document decisions, and communicate design trade-offs.

Key Features

  • Model software architecture across structure, behavior, data, and deployment using C4 and complementary notations
  • Combine the C4 model, viewpoints, and perspectives to assess design trade-offs
  • Document architecture decisions and model a marketing system using seven viewpoints
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Build software architecture models that connect system structure, behavior, data, and deployment. Using C4 diagrams, viewpoints, and perspectives, you'll learn to communicate design decisions, evaluate trade-offs, and create architecture views that answer stakeholder questions. Explore software architecture fundamentals, abstraction levels, metamodels, notation, and visual communication. Learn the C4 model alongside Viewpoints and Perspectives, with introductions to 4+1 and TOGAF. Combine C4 with architecture viewpoints to answer different stakeholder questions. Use diagram conventions, architecture decision records, version control, and model maintenance to document software architectures clearly. Work with seven viewpoints: Functional, Context, Information, Operational, Concurrency, Development, and Deployment. Apply perspectives for security, cost, performance and scalability, availability and resilience, evolution, development teams, and internationalization and location to examine concerns across views. Put the method into practice by modeling a marketing system. Draw each view, compare it with the author's example, refine your model, and communicate system design choices to technical and business audiences.

What you will learn

  • Create connected software architecture diagrams
  • Choose appropriate C4 levels for different audiences
  • Model all seven architecture viewpoints
  • Apply perspectives to examine cross-cutting trade-offs
  • Document architecture decisions and maintain models
  • Use views to communicate system design decisions
  • Model a marketing system using a viewpoint-based method

Who this book is for

For software architects, engineers, technical leads, engineering managers, and developers moving into architecture work. Consultants, product owners, business analysts, and CTOs who need to understand or communicate system structure and design decisions will also benefit. A basic understanding of software systems is enough; no prior modeling experience is required.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Quantum and AI Trust Fabric : The Enterprise Trust Fabric Protocol for the post-quantum, AI-native era (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984499 Editeur : BPB Publications

Description
Artificial intelligence and quantum computing are reshaping the digital enterprise, creating unprecedented opportunities while introducing new cybersecurity, privacy, governance, and resilience challenges. As organizations become increasingly AI-native, establishing trust across people, data, applications, infrastructure, and decisions has become a strategic imperative.

This book presents a practical framework for securing the post-quantum, AI-native enterprise. The chapters explore the foundations of enterprise trust and cybersecurity; AI security, governance, and responsible AI; privacy and regulatory readiness; cloud, data, and application security; emerging quantum threats and post-quantum readiness; and trust-driven architecture, governance, and enterprise decision-making. Each chapter connects technology with business risk, providing practical insights, frameworks, and actionable approaches for building resilient and trusted digital enterprises.

By the end of this book, leaders and practitioners will be better equipped to assess cyber and AI risks, design trusted enterprise architectures, strengthen privacy and governance, prepare for quantum-era threats, and make informed trust decisions. Readers will gain a strategic mindset and practical skills to build, govern, and continuously strengthen trust across every enterprise transaction.

What you will learn
? Build trusted AI systems for enterprise use securely.
? Assess cyber risks in intelligent systems early now.
? Apply privacy governance to emerging tech safely today.
? Understand quantum security and post-quantum readiness for business.
? Evaluate federated edge explainable trust models well now.
? Secure AI agents using Quantum Compatibility Gateways.

Who this book is for
This book is for security architects, enterprise architects, AI professionals, cybersecurity practitioners, technology leaders, researchers, students, and senior IT decision-makers. It is especially useful for professionals designing trustworthy digital ecosystems and preparing organizations for AI-driven transformation, emerging quantum risks, and resilient technology adoption.

Table of Contents
1. The Case of Trust Fabric
2. Architecture Views and Design Principles
3. The Four Operating Planes
4. Core Capability Layers
5. Hybrid and Multi-cloud Integration
6. Plugins, Posture, and Privacy
7. SDKs, Flows, and the Technology Stack
8. Microservices, Data, and Event Architecture
9. APIs, Diagrams, and Security Architecture
10. Observability, Compliance, and DevSecOps
11. Resilience and Scaling
12. Roadmap, Differentiators, and Commercialization
13. The Complete Fabric and Conclusion
14. APIs, Algorithms, and Code
15. Operations, Testing, and Deployment Reference
16. The Investment Case
17. Adoption, Governance, and Extended Reference
18. Closing Reference
19. Research Frontiers and Future Innovation

]]> 2026-11-11T08:00:02.283 <![CDATA[ Designing Human-Centered AI Interfaces : Building understandable, trustworthy, and controllable AI products (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984500 Editeur : BPB Publications

Description
A few years ago, generative AI was a novelty. Today it sits inside the products we use every day. The systems we ship now are probabilistic, which means the same input does not always produce the same output. That single shift changes how users decide what to trust, how they recover from mistakes, and how much control they feel they have.

This book is a practical, design-first guide to building AI products that people can understand, trust, and direct. It covers foundations and mental models for probabilistic systems, followed by a library of interaction patterns for inputs, outputs, explainability, steerability, recommendations, memory, agents, multimodal experiences, and high-stakes domains like healthcare and finance. It also explores practical approaches to delivery, including prototyping with live models, scaling patterns through a design system, handing off to engineering, and where the field is heading next. Case studies are drawn from Spotify, GitHub Copilot, Perplexity, and other widely used products.

By the end, you will have the frameworks, checklists, and handoff templates to design, prototype, and evaluate AI features with confidence, collaborate well with engineering and data science, and ship products that are not only technically advanced but also ethical, accessible, and centered on human needs.

What you will learn
? Master practical patterns for prompts, feedback, and explainability.
? Design steerable, interruptible UX that keeps humans in control.
? Visualize uncertainty and turn AI errors into trustworthy moments.
? Apply evaluation rubrics for usefulness, accuracy, safety, and delight.
? Build citations, provenance, and transparency directly into the interface.
? Scale AI patterns through design systems with consistent governance.
? Hand off to engineering with data contracts and guardrails.
? Prototype, test, and measure AI features using live models.

Who this book is for
This book is for product designers, UX professionals, UI designers, HCI researchers, and design leaders building AI-powered products. It is also useful for product managers seeking a design-oriented view of AI and front-end engineers collaborating with design teams. Familiarity with digital product design is recommended; a machine learning background is not required.

Table of Contents
1. Modern AI UX Landscape
2. AI Fundamentals for Designers
3. Human-centered AI for Trust and Transparency
4. Designing AI Inputs
5. Designing AI Outputs
6. Explainability in the Interface
7. Steerability and Interruptibility in AI Interfaces
8. Designing Recommendation and Personalization Engines
9. Memory and User Profiles in Conversational AI
10. Agent UX for Goals, Plans, and Safe Autonomy
11. Multimodal Interfaces
12. AI UX for Safety, Compliance, and Ethics
13. Prototyping and Evaluating AI UX
14. AI in Design Systems and Scaling Patterns
15. Handoff to Engineering
16. Future Trends in AI Interaction

]]> 2026-11-11T08:00:02.283 <![CDATA[ 40 fonctions Google Sheets : Fiches de révision détaillées ]]> https://univ.scholarvox.com/catalog/book/88983968 Editeur : AZURATEC

Maîtrisez les fonctions essentielles de Google Sheets, simplement et efficacement.

Conçues pour les débutants et testées en formation, ces 40 fiches détaillées vous offrent une synthèse claire et pratique des fonctions incontournables de Google Sheets.

Chaque fiche va à l’essentiel : explication simple, syntaxe, exemples concrets et conseils d’utilisation. Idéales pour accompagner un cours, réviser une certification ou gagner du temps au quotidien en entreprise, elles constituent un outil de référence fiable et immédiatement opérationnel.

Que vous soyez salarié, demandeur d’emploi, étudiant ou formateur, ce guide vous permet de mémoriser durablement les fonctions les plus utiles et de les mobiliser en situation professionnelle.

L’essentiel de Google Sheets, en 40 fiches claires et ciblées.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Débuter sur Excel 365 : Méthode simple détaillée par étapes ]]> https://univ.scholarvox.com/catalog/book/88983970 Editeur : AZURATEC

Ce guide décrit simplement et par étape la découverte du logiciel Excel pour que le débutant puisse apprendre par l'exemple.

  • Nous partons du principe que le meilleur moyen d'apprendre est de montrer comment le faire et de vous demander de le faire.
  • L’objectif de ce livret est de vous amener à manipuler diverses commandes et fonctionnalités très simplement.
  • Le classement par chapitre vous permettra de revenir sur des commandes précises pour la nécessité d’une révision ou d’une application sur un autre travail.
  • Ce guide est utilisable par tous, salariés, demandeurs d’emploi, retraités, étudiants, lycéens et collégiens.
  • En démarrant d’un classeur vide, le livret vous montre comment construire étape par étape votre tableau et le faire évoluer.
  • Il a été conçu pour vous permettre de découvrir seul le logiciel, ou réviser en complément d’une formation, ou pour préparer le passage d’une certification.

Tout en couleurs - Illustré par des captures d'écrans
Les tableaux réalisés dans ce livre sont téléchargeables à partir de 3 liens proposés dans d'ouvrage.

]]> 2026-11-11T08:00:02.283 <![CDATA[ The Agentic CFO : How Finance Leaders Build AI-Native Organisations That Govern, Scale, and Compound ]]> https://univ.scholarvox.com/catalog/book/88984029 Editeur : Packt Publishing

Build an AI-native finance function that can govern, automate, and scale by designing the data foundations, controls, audit trails, and human accountability every finance agent needs

Key Features

  • Build the data and governance foundations required for finance AI
  • Apply agentic AI across FP&A, close, tax, AP/AR, and treasury
  • Design human oversight, audit trails, and controls before scaling AI

Book Description

AI can automate finance workflows, but autonomy without strong foundations creates new risks. The Agentic CFO shows finance leaders how to build an AI-native finance function where agents operate within clearly defined data, governance, accountability, and control structures. You’ll begin by addressing the problems that undermine finance AI initiatives before they scale: inconsistent data, unclear ownership, poor process documentation, and weak accountability. You’ll then design the governance architecture around autonomous agents, including human authority lines, audit trails, escalation paths, and agent ownership. The book applies these principles to the finance workflows where AI can have the greatest impact, including financial close, FP&A, tax, AP/AR automation, treasury, and spend management. You’ll see how to determine what agents can handle autonomously, what requires human judgment, and how to make automated decisions auditable. Finally, you’ll explore the evolving role of the CFO, build a self-improving finance system, and establish proof before expanding AI across the organization.

What you will learn

  • Prepare finance data and processes for agentic AI
  • Define human authority before deploying autonomous agents
  • Build auditable decision trails for finance AI
  • Apply AI agents to close, FP&A, tax, and AP/AR
  • Introduce agentic AI into treasury and spend management
  • Establish ownership, escalation paths, and governance
  • Build finance systems that improve with every cycle
  • Prove AI outcomes before scaling across the organization

Who this book is for

This book is for CFOs, controllers, VPs of Finance, FP&A leaders, finance transformation leaders, and senior finance professionals responsible for introducing AI into the finance function. It is particularly relevant to leaders moving beyond AI pilots toward governed, production-ready agentic workflows. No AI development or programming experience is required.

]]> 2026-11-11T08:00:02.283 <![CDATA[ ServiceNow Application Development : A practical guide to building applications with JavaScript and automation, scoped applications automation and Fluent DSL, data modeling, scripting, and flow automation (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984504 Editeur : BPB Publications

Description
ServiceNow provides many out-of-the-box products to digitalize and transform your business. ServiceNow platform has a distinct advantage as an application platform as a service (aPaaS). It can swiftly customize out-of-the-box solutions or build custom applications. These capabilities enable developers to build connected solutions for their unique business needs. The platform also provides native AI capabilities to accelerate application development and enrich the end-user experience.

The book starts with a basic introduction to the ServiceNow platform and administration capabilities. Then it covers low-code development and progresses through advanced scripting concepts. Towards the end, you will get an overview of the native AI features, external version control, and modern development capabilities.

By the end of this book, you will turn into a ServiceNow development wizard and gain knowledge to develop and customize ServiceNow applications. The concepts will also help you prepare for relevant ServiceNow certifications. You will be amazed by the platform's full-stack development capabilities.

What you will learn
? Pro-code and low-code development with best practices and exercises.
? Code management and distribution.
? Automated testing in ServiceNow platform.
? Reports and analytics capabilities.
? Native GenAI capabilities of the ServiceNow platform.
? Modern development using Fluent and Now-SDK.

Who this book is for
The book is suited for ServiceNow professionals, students, and anyone looking to learn or master ServiceNow platform development. The reader does not need any experience with ServiceNow. Since the development on the Now Platform is based on JavaScript, a basic knowledge of JavaScript is required to learn pro-code concepts.

Table of Contents
1. ServiceNow Introduction
2. Applications in ServiceNow
3. User Administration
4. Data Model
5. User Interface
6. ServiceNow Scripting
7. Security and Governance
8. Workflow Automation
9. Automated Testing
10. Troubleshooting and Debugging
11. Integration
12. Enhanced User Experience
13. Reporting and Analytics
14. Source Code Integration and Distribution
15. Fluent-ServiceNow DSL
16. Now Assist – GenAI
17. Useful References and Purpose

]]> 2026-11-11T08:00:02.283 <![CDATA[ Mastering C# : From .NET internals to high-performance, secure C# applications (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984506 Editeur : BPB Publications

Description
C# enables developers to build performant, secure, scalable applications using the .NET runtime. Understanding the underlying runtime and performance-oriented features of modern C# helps developers create applications that are fast, responsive, concurrent, maintainable, and reliable.

This new edition provides an inside perspective on modern C# and the .NET runtime, beginning with the CLR, managed heap, and garbage collection. It then covers control structures, object-oriented design, arrays, generic collections, properties, indexers, tuples, and closures. In this new edition, readers will learn performance-oriented constructs such as span and memory, along with exception handling, regular expressions, and cryptography. The book further explores delegates, events, reflection, attributes, expression trees, asynchronous programming, parallel programming, multi-threading, LINQ, and lambdas for building responsive and concurrent systems. It also covers Visual Studio IDE and Visual Studio 2022, and features like unsafe code, pointers, dynamic programming, preprocessor directives, and new lock type semantics.

By the end of this book, readers will be able to write clean and maintainable C# applications. They will also be able to use advanced C# features for production-oriented development.

What you will learn
? Understand CLR execution and .NET garbage collection mechanics.
? Build reusable types with generics, properties, indexers, and interfaces.
? Develop responsive applications using async, multithreading, TPL, and PLINQ.
? Master secure I/O, serialization, and encryption with .NET modern libraries.
? Using Benchmark.NET to measure and optimize C# code for production systems.

Who this book is for
This book targets intermediate developers, C# developers, and students aiming to master the language in this new edition. Readers should have knowledge of basic programming logic and techniques, with experience in any programming language being an added advantage.

Table of Contents
1. Microsoft .NET Ecosystem
2. Getting Started
3. Classes and Objects
4. Object-oriented Programming
5. Arrays
6. Properties and Indexers in C#
7. Generics
8. Asynchrony and Parallel Programming in C#
9. Exception Handling in C#
10. Programming LINQ
11. Events and Delegates in C#
12. Lambda Expressions, Anonymous Methods and Expression Trees
13. Collections in C#
14. Multithreading in C#
15. File I/O and Serialization in C#
16. Operator Overloading
17. Attributes in C#
18. Reflection in C#
19. Span and Memory in C#
20. Regular Expressions
21. Cryptography
Appendix

]]> 2026-11-11T08:00:02.283 <![CDATA[ Practical Business Finance with AI : Use financial statements, budgets, forecasts, KPIs, and AI to make better business decisions ]]> https://univ.scholarvox.com/catalog/book/88984440 Editeur : Packt Publishing

Turn financial information into actionable business insights using statement analysis, budgeting, forecasting, KPIs, and AI-supported decision-making.

Key Features

  • Interpret financial statements and assess business performance with confidence
  • Build budgets, forecasts, and cost analyses to support informed decisions
  • Apply KPIs, financial analytics, and AI to generate business insights
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Build the financial intelligence to understand what business numbers mean and use them to make informed decisions. This book takes you from essential accounting concepts to practical financial analysis, planning, performance evaluation, and AI-supported decision-making. You’ll learn to interpret income statements and balance sheets, assess cash flow and financial health, and evaluate costs, profitability, and break-even points. You’ll then develop budgeting and forecasting skills for financial planning and use financial analytics and KPIs to turn performance data into useful business insights. The book also shows you how AI can support financial analysis and decision-making. Rather than treating finance concepts in isolation, it connects them through practical business applications so you can relate financial information to planning and performance decisions. Written by Valentine Ifenna Okolo, a multidisciplinary business professional with nearly two decades of experience across banking, accounting, financial analysis, audit, control, compliance, and business systems, the book concludes with a capstone financial analysis and business review that brings the core concepts together.

What you will learn

  • Interpret income statements and balance sheets
  • Assess cash flow and overall financial health
  • Build budgets and forecasts for financial planning
  • Analyze costs, break-even points, and profitability
  • Use financial analytics and KPIs to assess performance
  • Apply AI to financial analysis and decision support
  • Turn financial findings into business recommendations

Who this book is for

Managers, team leaders, entrepreneurs, business owners, project managers, financial analysts, aspiring FP&A professionals, and consultants who want to interpret financial information and make better business decisions will benefit from this book. It is particularly suited to non-finance professionals seeking stronger financial and business acumen. No formal finance or accounting background is required.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Graph Engineering for Agentic AI Systems : Design the topology. Don't inherit it. ]]> https://univ.scholarvox.com/catalog/book/88984459 Editeur : Packt Publishing

Design reliable agentic AI systems with explicit graph topology, controlled multi-agent orchestration, knowledge graph memory, and verifiable execution paths

Key Features

  • Choose agent orchestration patterns that justify their complexity and cost
  • Apply graph design through worked examples, chapter exercises, and companion code
  • Test workflow paths, recovery behavior, and approval gates before agents take action

Book Description

Agentic AI systems need more than capable models: they need clear rules for how work runs. Graph Engineering for Agentic AI Systems helps you design that structure, starting with when a single-agent loop is enough and when multi-agent orchestration earns its complexity. Distinguish execution graphs, knowledge graphs, and learned graphs, then explore typed state, routing, parallel fan-out and joins, and human approval gates. Compare runtimes including LangGraph, Google ADK, Microsoft Agent Framework, and the OpenAI Agents SDK. Worked examples, chapter exercises, and companion code connect architecture to implementation. Explore knowledge graph memory and GraphRAG, checkpointing, bounded retries, idempotency, and production observability. Examine permissions and authorization boundaries, then evaluate execution traces, node contracts, and stop conditions. By the end, you'll be equipped to design, test, and operate agent workflows with explicit topology, controlled side effects, and traceable decisions.

What you will learn

  • Choose when to keep an agent loop or introduce a graph
  • Design typed state, routing, parallel fan-out, and joins
  • Compare agent runtimes for orchestration and persistence
  • Build knowledge graph memory with typed, traceable facts
  • Operate workflows with checkpoints, retries, and idempotency
  • Verify execution paths, permissions, and human approval gates

Who this book is for

This book is for AI engineers, LLM application developers, software architects, and platform engineers building agent workflows beyond the prototype stage. Security engineers will also benefit from its treatment of agent identity, authorization, and approval boundaries. Familiarity with Python, LLM applications, and software architecture will help you engage with the code examples and exercises.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Learning Data Science with R and Python : Explore theory and practice of data science through R and Python (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984493 Editeur : BPB Publications

Description
Data science has become a key technology in the digital era and has applications in all industries ranging from health, finance, and retail to high-tech. As organizations face increasing competition in a technology-driven market, they are turning to data-driven approaches to tackle real-world problems. As the volume of available data continues to grow, so too does the array of tools for processing it, including popular languages like R and Python.

This book guides readers through the entire data science workflow using both R and Python. The book first introduces readers to setting up the right environment and the basic programming skills needed for the data science workflow. It also covers data wrangling and cleaning, exploratory data analysis, and data visualization to gain insights and communicate findings. Finally, you will learn the basic statistical modeling concepts, including probability distributions, hypothesis testing, and simple regression analysis. This book goes beyond the basics and covers machine learning. It covers key elements of R and Python, including syntax, data manipulation and visualization, essential statistical functions, and machine learning packages. It uses real-world data science problems as examples of how to apply these concepts.

By the end of this book, you will be able to analyze data, program in R and Python, and effectively present results using real-world scenarios.

What you will learn
? Set up R and Python data science environments.
? Understand core programming concepts in R and Python.
? Clean, transform, and manipulate real-world datasets.
? Perform exploratory data analysis to uncover patterns.
? Create effective visualizations using modern plotting libraries.
? Apply statistical techniques for inference and modeling.
? Build and evaluate machine learning models efficiently.

Who this book is for
This book is for students, aspiring data scientists, data analysts, software engineers, researchers, and business analysts who want to strengthen their skills. No prior R or Python experience is required, though basic computer literacy and fundamental mathematics knowledge are recommended.

Table of Contents
1. Getting Started with R
2. Core Programming in R
3. Data Wrangling with R
4. Exploratory Data Analysis in R
5. Data Visualization with R
6. Statistical Modeling and Analysis in R
7. Machine Learning with R
8. Getting Started with Python
9. Core Programming in Python
10. Data Handling with Pandas
11. Exploratory Data Analysis in Python
12. Data Visualization with Python
13. Statistical Analysis in Python
14. Machine Learning with Python
15. Syntax and Language Design
16. Data Manipulation and Wrangling
17. Visualization Capabilities
18. Statistical Analysis Tools
19. Machine Learning Libraries
20. Use Case-Driven Comparison

]]> 2026-11-11T08:00:02.283 <![CDATA[ 40 fonctions Excel : Fiches de révision détaillées ]]> https://univ.scholarvox.com/catalog/book/88983969 Editeur : AZURATEC

Maîtrisez les fonctions essentielles d’Excel, simplement et efficacement.

Conçues pour les débutants et testées en formation, ces 40 fiches détaillées vous offrent une synthèse claire et pratique des fonctions incontournables d’Excel.

Chaque fiche va à l’essentiel : explication simple, syntaxe, exemples concrets et conseils d’utilisation. Idéales pour accompagner un cours, réviser une certification ou gagner du temps au quotidien en entreprise, elles constituent un outil de référence fiable et immédiatement opérationnel.

Que vous soyez salarié, demandeur d’emploi, étudiant ou formateur, ce guide vous permet de mémoriser durablement les fonctions les plus utiles et de les mobiliser en situation professionnelle.

L’essentiel d’Excel, en 40 fiches claires et ciblées.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Loop Engineering for AI Agents : Design Reliable Agentic AI Systems Beyond the Prompt ]]> https://univ.scholarvox.com/catalog/book/88984444 Editeur : Packt Publishing

Design reliable agentic AI loops that act autonomously while remaining bounded, observable, independently verifiable, and accountable.

Key Features

  • Apply a minimum-sufficient-loop framework, adding complexity only when measurable failures justify it
  • Connect agentic AI architecture with organizational design and verified-outcome economics
  • Build reliable loops with independent verification, protected evidence, budgets, and escalation
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Designing reliable agentic AI systems requires more than writing the perfect prompt. The central challenge is designing the control system around a probabilistic worker: what it may do, what evidence proves completion, when it may retry, how much it may spend, what state must persist, and when a human must take over. This book provides a practical framework for addressing these challenges. Loop Engineering for AI Agents starts by defining loop engineering and separating it from prompt, context, and harness engineering. You'll examine how persistent AI loops change team structures, governance, management, and the economics of software production. The practical sections turn these principles into implementation through the Ralph fresh-context pattern, local loop architecture, protected verification, durable plans, retry and recovery policies, observability, prompt-injection boundaries, and human escalation. Written by Enrico Papalini, who has over 25 years of software engineering experience, the book provides a signal-first framework for adding only the mechanisms that justify their cost. By the end, you'll understand how to delegate autonomy through explicit completion contracts, independent evidence, bounded resources, authorization policies, reversible state transitions, and accountable human ownership.

What you will learn

  • Define bounded completion contracts for agentic work
  • Separate workers from verifiers using external evidence
  • Persist intent, state, lessons, and checkpoints outside model context
  • Design retry, resume, rollback, and budget policies
  • Protect specifications, verifiers, and authorization policies
  • Choose between fresh-context loops and full orchestration
  • Measure cost per accepted outcome instead of token usage
  • Design human-escalation policies for reliable autonomy

Who this book is for

Engineering leaders evaluating or scaling agentic development workflows, senior software engineers and architects designing AI-agent control systems, and platform and developer-experience engineers building internal AI infrastructure. AI, risk, security, and governance specialists working in controlled or regulated environments will also benefit. Familiarity with software engineering principles and AI-assisted development workflows is recommended.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Modern Angular Design Patterns : Craft scalable and high-performance web applications with Angular best practices (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984494 Editeur : BPB Publications

Description
Angular has become the foundation of scalable web applications, and mastering its modern paradigms alongside design patterns is essential for frontend developers. Its standalone APIs and Signals have redefined how we structure application code. Knowing how to properly architect enterprise-grade Angular applications is a key differentiator for creating high-performance and maintainable software.

This book helps to bridge any gaps by mapping proven software design patterns to real Angular code. The book begins with fundamentals and TypeScript features, and it further explores component-driven development, reusable services, and standalone components with providers. You will also explore reactive programming with RxJS and Signals best practices, state management with NgRx, routing, lazy loading, and performance optimization. Finally, the book covers forms, data transformation with the Model-Adapter pattern, advanced behavioral patterns, micro-frontends with Angular Elements, and real-world enterprise case studies that bring all these concepts together.

By the end of this book, you will possess a deep, practical mastery of Angular architectural patterns and best practices, empowering you to confidently build, optimize, and scale production-ready applications in enterprise environments.

What you will learn
? Apply classic and advanced design patterns in modern Angular applications.
? Build standalone components, directives, and provider-based architectures.
? Manage reactive state with RxJS, Signals, and NgRx.
? Optimize performance with lazy loading and route guards.
? Design reusable services using dependency injection effectively.
? Apply testing and error-handling best practices for robust apps.
? Transform API data using the Model-Adapter pattern.

Who this book is for
This book is for intermediate-to-advanced Angular developers, frontend and full-stack engineers, software architects, and technical leads aiming to build scalable enterprise applications. Readers should have a basic working knowledge of Angular, TypeScript, and JavaScript.

Table of Contents
1. Angular Fundamentals and Design Patterns
2. TypeScript Patterns for Angular
3. Component-Driven Development
4. Reusable Services
5. Building with Standalone Components and Providers
6. Reactive Programming with RxJS
7. State Management with NgRx
8. Routing with Angular Standalone APIs
9. Lazy Loading and Performance Optimization
10. Forms and User Input
11. Data Transformation and APIs
12. Advanced Behavioral Patterns
13. Micro-frontends with Angular Elements
14. Real-world Case Studies

]]> 2026-11-11T08:00:02.283 <![CDATA[ Automated Testing for AI Systems : Building reliable AI with continuous testing, behavioral validation, and real-time guardrails (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984505 Editeur : BPB Publications

Description
As AI transitions from experimental projects into mission-critical applications across modern industries, traditional software testing techniques are proving inadequate for probabilistic machine learning, generative AI, and agentic AI systems. This book addresses this reliability gap by introducing AI TestOps—a specialized discipline designed to eliminate AI Quality Debt and build scalable AI solutions.

This book introduces AI TestOps, a practical engineering discipline for testing AI systems throughout their lifecycle. It explains how to validate data, features, models, behaviors, fairness, explainability, and guardrails using repeatable engineering practices. Readers will learn practical frameworks such as the Feature to Representation Integrity Model (FRIM), the Behavior Validation Loop (BVL), continuous testing and training (CTT), and Auto-Human-in-the-Loop (Auto-HITL), along with guidance on integrating testing into modern AI delivery pipelines using widely adopted open-source tools.

By the end of this book, readers will have a structured approach for designing, validating, deploying, and operating trustworthy AI systems. More importantly, they will understand how to build testing into AI engineering from day one, helping organizations deliver AI solutions that remain reliable as they evolve.

What you will learn
? Build automated testing pipelines for ML, LLM, and agentic AI.
? Detect behavioral drift using continuous evaluation techniques.
? Integrate AI testing into CI/CD and continuous testing and training.
? Apply guardrails and human oversight to high-risk AI systems.
? Validate representations using FRIM inside CTT pipelines.
? Stress-test system robustness using the Resilience Evaluation Loop.

Who this book is for
This book is intended for AI engineers, machine learning engineers, MLOps professionals, software architects, QA engineers, technical leaders, students, and engineering managers responsible for developing or operating AI systems. A foundational background in machine learning concepts and software engineering practices helps readers maximize value from the material.

Table of Contents
1. AI Quality Crisis
2. Rethinking Software Testing for AI
3. Principles of AI Test Engineering
4. Data Validation and Quality Contracts
5. Feature Integrity and Pipeline Testing
6. Model Validation and Regression Testing
7. Continuous AI TestOps Engine
8. Bias and Fairness Testing
9. Adversarial Testing and Robustness
10. Explainability and Transparency Testing
11. AI Guardrails and Ethical Assurance
12. AI Evaluation and Observability
13. AI TestOps for Third-party and Vendor Models
14. Future of AI TestOps

]]> 2026-11-11T08:00:02.283 <![CDATA[ Data Architecture for Data Engineers : Practical approaches to building scalable, efficient data solutions with real-world applications ]]> https://univ.scholarvox.com/catalog/book/88984434 Editeur : Packt Publishing

Develop practical data architecture skills for modern data platforms and learn how to build governed, reliable, and scalable systems while aligning technical choices with organizational goals and operational requirements

Key Features

  • Evaluate architecture options using structured reviews and decision frameworks
  • Build governed, reliable data platforms with clear ownership and accountability
  • Apply architecture patterns to solve real-world business and operational challenges

Book Description

Data Architecture for Data Engineers helps data engineers move beyond pipelines and tools to understand how scalable data solutions are designed. Starting with the foundations, you will learn the principles behind data architecture, including governance, data quality, security, scalability, accessibility, lineage, and stewardship. You’ll then work through data modeling and database design, comparing conceptual, logical, and physical models, relational databases, NoSQL databases, columnar databases, normalization, denormalization, and trade-offs behind design choices. The book then moves into data pipelines, covering ingestion, processing, storage, ETL, ELT, batch and real-time processing, monitoring, optimization, and robust design. You will compare data lakes, data warehouses, lakehouses, data mesh, and data fabric, and see where AWS, Azure, Databricks, Delta Lake, Snowflake, and pipeline tools fit into modern architectures. Practical scenarios show how industries shape requirements and why architecture must follow the use case, not trend. By the end, you will have a view of data solutions and a framework for choosing solutions that fit business needs.

What you will learn

  • Assess architecture options using practical decision frameworks
  • Align technical designs with business priorities and operational constraints
  • Choose suitable models for relational, NoSQL, and columnar data
  • Design reliable batch, streaming, ETL, and ELT pipelines
  • Compare warehouses, lakes, lakehouses, mesh, and fabric patterns
  • Apply governance, quality, security, and ownership principles
  • Use decision frameworks to review and improve data platforms

Who this book is for

Data Architecture for Data Engineers is for data engineers who want to expand beyond implementation and contribute to architecture, technical strategy, and platform design. It is ideal for professionals working with platforms, pipelines, databases, analytics, or cloud services who want to understand how systems are planned, governed, and evolved. Readers should know basic data engineering concepts, SQL, and cloud fundamentals. It also benefits aspiring data architects, analytics engineers, BI developers, analysts, software engineers, and technical managers seeking practical design guidance.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Cloud AI Architect : Designing enterprise AI systems across Azure, AWS, and GCP (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984498 Editeur : BPB Publications

Description
Enterprise AI has moved out of research notebooks into production, running on cloud infrastructure that demands a specialized multi-cloud strategy. This book is your definitive architectural roadmap to designing, building, and operating production-ready artificial intelligence solutions across Microsoft Azure, AWS, and Google Cloud Platform without vendor lock-in.

This book systematically covers the complete multi-cloud AI lifecycle across twelve detailed chapters. It begins with core infrastructure—GPU compute, petabyte-scale storage, private subnets, and SRE resilience—then layers on Zero Trust identity, enterprise governance, and cost management alongside LLM inference optimization. It moves through a managed platform overview comparing Azure OpenAI, AWS SageMaker, and Google Vertex AI, leading directly into hands-on model development, feature engineering, and AutoML pipelines. Later chapters cover domain-specific architectures for enterprise vision, natural language, and predictive forecasting, along with no-code/low-code tools like Power Automate and Textract.

By the end of this book, you will be able to design AI systems that are fast, affordable, governed, and resilient, and to defend every architectural choice with evidence rather than vendor claims.

What you will learn
? Optimize GPU compute, storage, and networking for AI workloads.
? Enforce Zero Trust identity, governance, and compliance across three clouds.
? Build and deploy models on Azure ML, SageMaker, and Vertex AI.
? Design production vision, language, and forecasting systems end-to-end.
? Ship faster with no-code and low-code AI tooling.

Who this book is for
This book is written for students, cloud architects, machine learning engineers, site reliability engineers, data platform leads, and engineering managers accountable for AI systems in production. Comfort with cloud fundamentals and Python will help, but no prior experience building an AI platform is assumed.

Table of Contents
1. Core Cloud Infrastructure for AI Workloads
2. Identity, Governance, Compliance and Access Control
3. Cost Management, Monitoring, and Operational Excellence
4. Overview of Azure, AWS, GCP, the AI Platforms
5. Hands-on Model Building with Azure and AWS
6. Model Development with GCP Vertex AI and AutoML
7. Enterprise Vision AI Architecture
8. Enterprise Language and Conversational AI Architecture
9. Predictive Analytics and Forecasting Architecture
10. No-code and Low-code AI Engineering
11. Benchmarking and Platform Selection
12. Multi-cloud AI Architecture and Future Trends

]]> 2026-11-11T08:00:02.283 <![CDATA[ Vision Language AI : Building real-world multimodal applications from theory to production (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984502 Editeur : BPB Publications

Description
While most resources treat vision and language as separate domains, the future of AI demands their seamless integration. Vision Language Models (VLMs) represent the frontier of AI, revolutionizing industries from healthcare to autonomous systems, but implementing them remains a complex challenge. This includes integration challenges, deployment complexity, and real-world performance issues. This book helps you move from theoretical AI concepts to production-ready systems.

This book takes you from foundational concepts to deployed, scalable VLM systems through real-world projects across five industries. You will explore the complete development lifecycle from data preprocessing and model architecture to optimization techniques and production deployment strategies. Designed around practical application, every chapter provides battle-tested solutions, complete code implementations, and adaptable architectural patterns that work in live environments. The book will help you build the multimodal AI applications that will define the next decade of technology.

By the end of this book, you will possess the practical skills and architectural expertise needed to build, scale, and manage production-ready Vision Language Models safely and effectively across any domain.

What you will learn
? Build, fine-tune, and deploy VLMs from scratch.
? Master multimodal training, prompt engineering, and optimization.
? Deploy on cloud and edge with monitoring and security.
? Apply VLMs across healthcare, retail, and manufacturing.
? Design agentic AI systems that reason and act autonomously.

Who this book is for
This book is for current and aspiring AI/ML engineers, computer vision specialists, data scientists, technical product managers, and software architects with a basic foundation in Python programming, fundamental machine learning concepts, and deep learning principles using PyTorch.

Table of Contents
Part I: Fundamentals
1. Vision-Language Revolution
2. Understanding Vision-Language Architecture
3. Essential Models and Frameworks
4. Data Engineering for Multimodal AI
Part II: Implementation
5. Building VLMs and Agentic AI from Scratch
6. Transfer Learning and Fine-tuning
7. Advanced Training Techniques
8. Model Evaluation and Benchmarking
9. Prompt Engineering and Interaction Design
Part III: Optimization
10. Performance Optimization
11. Scaling and Distributed Systems
12. Edge Deployment and Mobile Integration
Part IV: Deployment
13. Cloud Deployment Strategies
14. Monitoring and Maintenance
15. Privacy and Security
Part V: Business Applications
16. Healthcare Applications
17. Autonomous and Agentic Systems
18. E-commerce and Retail
19. Manufacturing and Quality Control
20. Content Creation and Media
21. Ethical and Responsible AI
Appendix A. Notations and Metrics
Appendix B. Hardware and Software Setup
Appendix C. Troubleshooting
Appendix D. Further reading
Appendix E. Using the Code Repository

]]> 2026-11-11T08:00:02.283 <![CDATA[ Siri AI Visual Bible : A step-by-step, screenshot-by-screenshot guide to getting things done across your Apple devices ]]> https://univ.scholarvox.com/catalog/book/88984018 Editeur : Packt Publishing

Use Siri AI to find information, create useful content, organize work, automate routines, and manage everyday life across iPhone, iPad, and Mac

Key Features

  • Follow short visual lessons with readable screenshots, guided activities, and clear results
  • Create personal Shortcuts and Safari tools simply by describing what you want
  • Complete real work and family tasks with privacy checks before important actions

Book Description

Siri AI can help you do far more than answer simple questions. This practical visual guide shows you how to use it across your Apple devices to complete real work, reduce repeated effort, and stay organized. You will begin by checking device compatibility and setting up Siri AI around your preferences. Step-by-step lessons show you how to speak or type naturally, add useful context, ask follow-up questions, research unfamiliar subjects, compare options, and verify important information. You will also learn to find messages, emails, photos, notes, files, and details shown on your screen. The book then moves into productive everyday work. You will draft and improve documents, emails, reports, and presentations; summarize conversations, webpages, documents, and PDFs; prepare for meetings; capture follow-ups; and coordinate tasks across Files, Notes, Calendar, and Reminders. Next, you will create personal Shortcuts and Safari research tools simply by describing the result you want. Guided activities help you automate routines for work, study, freelancing, and family life. Finally, you will use Siri AI to organize appointments, travel, household responsibilities, spending comparisons, health-related reminders, and weekly priorities. Privacy checks and appropriate human advice are highlighted whenever an action deserves extra care.

What you will learn

  • Set up Siri AI and use it confidently across Apple devices
  • Ask naturally and improve results through follow-up requests
  • Find information, messages, photos, notes, and files faster
  • Create polished emails, documents, summaries, and presentations
  • Organize meetings, family plans, routines, and weekly priorities
  • Build useful Shortcuts without learning automation or coding
  • Create Safari research tools and reduce repeated work
  • Use privacy checks before important actions or sharing data

Who this book is for

This book is for professionals, managers, consultants, freelancers, educators, small-business owners, students, families, and everyday Apple users who want to save time, stay organized, and use Siri AI more effectively at work and at home. Readers should be comfortable using an iPhone, iPad, or Mac, but no experience with AI, automation, Shortcuts, or programming is required. Some lessons require an Apple Intelligence-compatible device, and the availability of individual features may vary by device, region, language, and software version.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Build Production-Ready AI Agents with the Microsoft Stack : Ground, secure, and evaluate agents with Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework ]]> https://univ.scholarvox.com/catalog/book/88984447 Editeur : Packt Publishing

Build AI agents across Copilot Studio, Microsoft Foundry, and Agent Framework, from tenant setup and deployment to multimodal assessment, code-first workflows, evidence-based decisions, and production architecture choices

Key Features

  • Build grounded, multimodal, and multi-agent solutions across three Microsoft agent tiers
  • Evaluate, secure, publish, and monitor agents using production-focused workflows
  • Implement durable human-in-the-loop orchestration with checkpoints and auditable decisions

Book Description

Moving an AI agent from a working prototype to a production system requires more than a good prompt. You need the right architecture, reliable grounding, controlled tool use, evaluation, security, observability, and clear points for human intervention. Build Production-Ready AI Agents with the Microsoft Stack shows how these pieces fit together through through Claims 365, a fictional insurer's claims process. You will build a grounded business agent with Copilot Studio, secure and evaluate it, and publish it for real use. You will then create a multimodal assessment agent in Microsoft Foundry that processes images and documents and returns structured outputs. From there, you will run, test, and resume a complete code-first multi-agent workflow built with Microsoft Agent Framework, in Python or C#. It coordinates agents and tools to gather and challenge evidence, then pauses for durable human approval using checkpoints, shared state, Dataverse, and an auditable investigation trail. The book concludes by comparing the three Microsoft agent tiers (Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework) and the trade-offs between flexibility, control, maintenance, and operational responsibility, helping you select the right architecture for different production scenarios.

What you will learn

  • Choose the right Microsoft agent tier for each workload
  • Ground agent responses in Dataverse and business data
  • Evaluate grounded responses and identify failure cases
  • Secure and publish agents with sign-in and entitlement checks
  • Build multimodal agents with structured JSON outputs
  • Design code-first multi-agent workflows with shared state
  • Add durable human approval with checkpoints and audit trails

Who this book is for

This book is for solution architects, AI engineers, developers, technical consultants, and Microsoft platform professionals who need to take agentic solutions beyond prototypes. It is especially useful for readers working across Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework who need to make architecture, security, evaluation, orchestration, and human-oversight decisions. Familiarity with Microsoft cloud services and application development will help readers get the most from the code-first sections.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Harness Engineering : Design Patterns for Securing Long-Horizon Multi-Agent AI Systems ]]> https://univ.scholarvox.com/catalog/book/88984454 Editeur : Packt Publishing

Build a secure control plane for agentic AI with design patterns for bounded autonomy, least privilege identity, trusted context, governed memory, and runtime recovery

Key Features

  • Design a security control plane for long running agents and delegated multi-agent work
  • Contain prompt injection and memory poisoning through enforceable trust boundaries
  • Use HarnessLab scenarios to connect design patterns with observable control decisions

Book Description

Long-running AI agents need controls that hold across tool calls, delegated tasks, and persistent memory. Harness Engineering presents design patterns for securing long-horizon multi-agent AI systems through a control plane that governs autonomy, identity, intent, context, and memory. Learn to scope agent permissions, contain prompt injection, detect goal drift, and place human oversight where consequential actions require it. Through architectural diagrams and HarnessLab scenarios, explore decision tracing, evaluation gates, runtime intervention, recovery, fleet coordination, and data governance. Build the engineering judgment to make agent behavior observable, authority accountable, and improvement measurable, so you can move from promising prototypes to controlled production operations.

What you will learn

  • Set autonomy budgets and risk based human approval gates
  • Scope agent identities, credentials, and delegation chains
  • Bind tool authority to intent and detect goal drift
  • Contain prompt injection and govern persistent memory
  • Use decision traces and evals to govern agent improvement
  • Steer live runs and enforce fleet and data boundaries

Who this book is for

For AI engineers, security architects, platform teams, and technical leaders designing or governing long running agentic AI systems. Researchers and graduate students will also benefit from its pattern based treatment of agent security and control. Familiarity with LLM applications, tool calling, access control, and distributed systems is recommended. The book supports readers moving from agent prototypes to accountable enterprise operations.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Learning Microsoft Power Automate : Build secure, scalable, and intelligent enterprise workflows with Microsoft Power Automate, AI Builder, and Copilot (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984488 Editeur : BPB Publications

Description
Microsoft Power Automate helps organizations reduce manual effort, improve consistency, and connect systems with minimal code. As automation adoption continues to grow, organizations increasingly require solutions that are secure, reliable, scalable, and aligned with business objectives.

This book covers cloud flows, desktop automation, and business process flows, with their practical implementations. This includes expressions and Power Fx, using AI Builder and Copilot to build intelligent automation solutions, along with advanced Power Automate Desktop techniques. It also covers integration patterns using APIs, databases, Python, and custom connectors, as well as credential management using Azure Key Vault and CyberArk. Operational topics such as monitoring, troubleshooting, governance, and deployment pipelines are also addressed. Throughout the book, practical examples and design considerations are drawn from real-world project scenarios.

By the end of the book, you will have a clear understanding of how to design, build, deploy, and manage automation solutions that hold up in production environments. The book is suitable for beginners, automation developers, business analysts, Power Platform professionals, and solution architects.

What you will learn
? Use Power Fx and expressions to enhance workflows.
? Create intelligent automation with AI Builder and Copilot.
? Integrate APIs, databases, Python, and custom connectors.
? Secure credentials using Azure Key Vault and CyberArk.
? Monitor, troubleshoot, and optimize automation performance.
? Implement governance and deployment pipelines effectively.
? Design scalable automation solutions for enterprise environments.

Who this book is for
This book is written for practitioners involved in designing, building, or maintaining automation solutions using Microsoft Power Automate. This includes automation developers, RPA developers, Power Platform professionals, business analysts, solution architects, and IT professionals working on process automation initiatives. It is suitable for students and those new to automation seeking practical guidance for real-world scenarios and enterprise-scale implementations.

Table of Contents
1. Introduction to Power Automate and Microsoft 365
2. Expressions and Power Fx
3. Building Your First Cloud, Desktop and Business Flows
4. Intelligent Automation with AI Builder and Copilot
5. Advanced Power Automate Desktop
6. Custom Connectors, Databases, and Python in Power Automate
7. Credential Management Azure Key Vault and CyberArk
8. Monitoring, Sharing and Troubleshooting Flows
9. Deployment Pipelines and Governance
10. Integrating with Microsoft 365 Applications
11. Enterprise Scale Automation Strategies
12. Future Trends and Emerging Technologies

]]> 2026-11-11T08:00:02.283 <![CDATA[ Practical Claude for Business : Build repeatable business workflows for marketing, sales, and research with Claude Skills and Cowork ]]> https://univ.scholarvox.com/catalog/book/88984441 Editeur : Packt Publishing

Streamline marketing, sales, research, and reporting with Claude through structured prompting, reusable Skills, and Cowork automation

Key Features

  • Turn research, marketing, and sales tasks into repeatable Claude workflows
  • Build reusable Claude Skills for consistent business documents and reports
  • Automate file and document tasks with Claude Cowork

Book Description

Replace scattered one-off prompts with repeatable Claude processes that help you research markets, plan campaigns, manage sales communication, and prepare reports. Through practical business examples, you'll learn to move from an AI-generated answer to work you can use. Digital marketing expert and AI educator Anton Voroniuk brings more than 17 years of industry experience to guide you. Begin with Claude fundamentals and structured prompting, then put those methods to work: compare competitors, define audiences, create SEO and paid advertising content, and build sales proposals. You'll also explore creative and visual projects to maintain a consistent brand across business materials. Next, package recurring tasks as Claude Skills, automate file and document workflows with Claude Cowork, and use Claude to clean data, audit spreadsheets, and prepare actionable reports. The final chapter explores how Make, n8n, browser agents, and other AI assistants can extend your setup. By the end, you'll know how to choose and combine these techniques for everyday business work.

What you will learn

  • Write structured prompts for reliable business outputs
  • Research markets and competitors to guide decisions
  • Plan marketing campaigns and content across channels
  • Create prospecting and sales communication workflows
  • Develop consistent brand and visual content with Claude
  • Build reusable Claude Skills for recurring business tasks
  • Automate document and file workflows with Claude Cowork
  • Analyze business data and audit spreadsheets with Claude

Who this book is for

Marketers, sales professionals, consultants, analysts, entrepreneurs, and operations teams who want to replace one-off AI prompts with repeatable workflows for research, content, customer communication, and reporting. No coding experience is required for the core examples; advanced integrations and design or code-based workflows may require additional tools and setup.

]]> 2026-11-11T08:00:02.283 <![CDATA[ The Blender 5 Environment Artist's Guide Part 2 : Complete a Victorian environment in Blender with assets, interiors, texturing, and final rendering ]]> https://univ.scholarvox.com/catalog/book/88984458 Editeur : Packt Publishing

Written by the author of the best-selling "Blender for Beginners" book, this second volume continues the Victorian city from Part 1, adding new buildings, hero assets, interiors, scene refinement, and the final render Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Continue the Victorian city from Part 1 with new shops, townhouses, a tower, and a vintage truck
  • Develop detailed exteriors and interiors while refining materials, architecture, and hero assets
  • Bring the full environment together with scene finishing, visual storytelling, and a polished final render

Book Description

A strong environment comes together when individual buildings, props, materials, and storytelling details feel like parts of one believable world. In Part 2, you continue the Victorian city begun in Part 1 and bring the larger environment to completion. The Blender 5 Environment Artist's Guide Part 2 expands the scene with a Victorian butcher's shop, a vintage truck, a detailed perfume shop, townhouses, and a prominent Victorian tower. As you build, texture, and refine these assets, you will work across both exteriors and interiors, develop architectural detail, and strengthen the visual consistency of the city. Each chapter adds another major piece to the environment while reinforcing practical workflows for modeling, texturing, asset creation, and scene organization in Blender 5. By the end of the book, you will have completed the full Victorian environment started in Part 1, refined its key structures and hero assets, and brought the entire scene together with final detailing and a polished render suitable for your portfolio. *Email sign-up and proof of purchase required

What you will learn

  • Build and detail a Victorian butcher shop with a complete exterior
  • Model and texture a vintage truck as a hero environment asset
  • Create a detailed perfume shop with exterior and interior elements
  • Construct Victorian townhouses with consistent architectural styling
  • Build and texture a landmark Victorian tower
  • Refine materials, facades, interiors, and architectural details
  • Integrate major assets while maintaining visual consistency across the scene
  • Finish the complete environment and produce a polished final render

Who this book is for

This book is for aspiring environment artists, 3D artists, game artists, Blender users, and digital creators continuing the Victorian city project from Part 1. It is suitable for beginners and intermediate users who want to strengthen their skills in complex asset creation, exterior and interior detailing, texturing, scene refinement, and final rendering for games, animation, visualization, or portfolio work. Familiarity with Blender's interface and the project built in Part 1 is recommended.

]]> 2026-11-11T08:00:02.283 <![CDATA[ Agentic AI Cookbook : 80+ practical recipes to build agents that learn, adapt, and collaborate (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984490 Editeur : BPB Publications

Description
Agentic AI represents the next major paradigm shift in artificial intelligence, moving from passive text generation to autonomous systems that reason, collaborate, and execute complex workflows. This book is your hands-on roadmap for bridging the gap between AI theory and building self-directed, real-world intelligent agents.

This cookbook pairs clear principles with more than eighty hands-on recipes. It builds the foundations of agentic AI and the MCP and A2A protocols, then tours the major frameworks: LangChain and LangGraph, the Microsoft Agent Framework, and CrewAI. It covers single-agent design and recipes, multi-agent communication patterns, and twenty multi-agent systems, then takes agents into the enterprise across procurement, HR, legal, sales, and finance, with a plane-based deployment architecture, security and threat modeling, and the governance, trust, and ethics that autonomy demands. It targets production-grade enterprise applications across procurement, legal, HR, finance, IT, and sales, while mastering containerization, API gateways, state scaling, prompt injection defense, RBAC, command sandboxing, and governance frameworks.

By the end of this cookbook, you will possess the practical skills to confidently design, build, and deploy secure, scalable, and collaborative agentic AI systems ready to solve complex enterprise challenges.

What you will learn
? Build agents with tools, memory, planning, and reasoning.
? Implement Model Context and Agent-to-Agent protocols.
? Orchestrate workflows with LangChain, Microsoft Agent Framework, and CrewAI.
? Apply single-agent and multi-agent communication design patterns.
? Deploy agentic systems across cloud, hybrid, and edge.
? Secure agents against prompt injection and data leakage.
? Evaluate, govern, and build trust in autonomous systems

Who this book is for
This book is for AI engineers, solution architects, and developers moving from prompt-based prototypes to production-grade autonomous agents. It also serves data scientists, technical leads, and product managers who design, deploy, or govern single-agent and multi-agent systems across enterprise business functions.

Table of Contents
1. The Evolution of Intelligence
2. Architecting the Agentic Stack
3. Standard Protocol in Agent AI
4. LangChain Framework
5. Microsoft Agent Framework
6. CrewAI and Emerging Open-source Framework
7. Single-Agent Design Principles
8. Recipes for Single-Agent Use Cases
9. Multi-Agent Communication Patterns
10. Multi-Agent Communication Patterns Recipes
11. Agentic AI in Business Functions
12. Architecting and Deploying Agentic Systems
13. Securing AI Agents
14. Building Trust and Ethical Considerations

]]> 2026-11-11T08:00:02.283 <![CDATA[ Decentralized Identity and Zero-Knowledge Proofs : Master self-sovereign identity and cryptographic privacy from theory to production (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984492 Editeur : BPB Publications

Description
Data breaches and centralized control have fractured digital trust. This book bridges that gap by exploring self-sovereign identity (SSI), decentralized identifiers (DIDs), and zero-knowledge proofs (ZKPs) as approaches to digital identity, privacy, and trust.

Beginning with the evolution of digital identity systems, the book establishes SSI principles, trust models, and decentralized identity before moving into DID methods, verifiable credentials, verifiable presentations, and DID authentication. It then builds the foundations of ZKPs through cryptographic foundations and proof systems, progressing to zk-SNARKs, zk-STARKs, circuit development using Circom and snarkjs, and advanced techniques such as recursive proof composition, lookup arguments, folding schemes, and ZK-rollups. The book then brings DIDs and ZKPs together through privacy-preserving credentials, selective disclosure, predicate proofs, and practical protocols.

By the end of this book, you will be fully equipped to architect, code, and deploy production-grade, privacy-preserving identity solutions, transforming how your organization manages trust, user privacy, and compliance.

What you will learn
? Architect self-sovereign identity systems using W3C DID standards.
? Design and optimize ZK circuits using Circom and snarkjs.
? Implement selective disclosure and predicate validation protocols.
? Deploy robust cryptography including Pedersen commitments and Merkle trees.
? Build anonymous KYC and voting application workflows.
? Scale decentralized identity frameworks securely.
? Protect AI models with zero-knowledge proofs.

Who this book is for
This book is for blockchain engineers, security architects, privacy engineers, and full-stack developers looking to build next-generation identity solutions. It is ideal for software professionals aiming to master cryptographic proofs and decentralized trust networks.

Table of Contents
1. Identity Crisis and Importance of Privacy
2. DID Core Concepts and Standards
3. DID Implementation
4. Zero-Knowledge Proof Fundamentals
5. Cryptographic Foundations for Zero-Knowledge Proofs
6. ZKP Proof Systems and Frameworks
7. Developing ZKP Circuits and Proofs
8. Advanced Topics in ZKP Systems
9. Integration Architecture and Design Patterns
10. Privacy-preserving Credentials
11. Advanced Integration Projects
12. Zero-Knowledge Machine Learning
13. Production Deployment and Scaling
14. Emerging Trends and Future Directions

]]> 2026-11-11T08:00:02.283 <![CDATA[ Learn GitHub Copilot : Build, test, secure, and scale software with AI-assisted workflows (English Edition) ]]> https://univ.scholarvox.com/catalog/book/88984507 Editeur : BPB Publications

Description
GitHub Copilot has rapidly evolved from a code-completion assistant into a practical engineering collaborator that spans the entire software development lifecycle. As teams face pressure to ship faster without sacrificing quality or security, mastering Copilot effectively has become a core professional skill for modern software teams.

This book takes readers from Copilot fundamentals and role-based usage into hands-on implementation across backend, frontend, design documentation, testing, and application security. It expands into databases, data science, machine learning, infrastructure, and configuration management, CI/CD automation, serverless systems, and structured debugging. It then tackles real-world challenges: migrating legacy frameworks, advanced workflow customization, extending Copilot through the CLI and IDE integrations, ethical usage, and agentic workflows across the GitHub platform.

By the end of this book, readers will be able to build a repeatable, Copilot-enabled workflow that improves development speed, code quality, and delivery confidence, combining AI acceleration with sound engineering judgment, security discipline, and responsible usage.

What you will learn
? Master Copilot setup, chat commands, and completions.
? Craft context-rich prompts for better, relevant outputs.
? Optimize product, backend, testing, and DevOps workflows.
? Debug faster with guided refactoring and review patterns.
? Reduce risk when migrating legacy frameworks and code.
? Implement secure, ethical, and enterprise-grade AI engineering standards.

Who this book is for
This book is for software developers, senior engineers, tech leads, QA professionals, DevOps engineers, students, and architects applying GitHub Copilot in real delivery workflows. Basic programming knowledge and familiarity with Git and GitHub are recommended before reading.

Table of Contents
1. Introduction to GitHub Copilot
2. Role-based Usage of GitHub Copilot
3. Copilot for Backend Development
4. Copilot for Frontend Development
5. Copilot for Design, Documentation, and Collaboration
6. Code Quality and Testing
7. Maintaining Application Security
8. GitHub Copilot Tips and Shortcuts
9. GitHub Copilot for Databases, Data Science and Machine Learning
10. Infrastructure and Configuration Management with Copilot
11. Automation and Serverless Systems using Copilot
12. Problem Solving and Debugging with Copilot
13. Migration of Legacy Languages and Frameworks
14. Advanced Customization and Efficiency
15. Extending, Building and Integrating with GitHub Copilot
16. Best Practices and Ethical Usage
17. Future View

]]> 2026-11-11T08:00:02.283 <![CDATA[ La blockchain dans l'industrie : Au service de la transformation digitale et de l'excellence opérationnelle ]]> https://univ.scholarvox.com/catalog/book/88919212 Editeur : Maxima

La révolution blockchain est là. Destiné à tous les acteurs de l’industrie, cet ouvrage explicite la combinaison gagnante blockchain / transformation digitale / excellence opérationnelle, pour générer et accroître la valeur ajoutée des entreprises par la création de nouveaux business et business modèles. Des méthodologies et des exemples fondés sur l’expérience y sont proposés, pour permettre de démarrer et piloter des projets sur la base de cette technologie dans un cadre de collaboration et de coopétition au sein de leur écosystème. Dans ce maître-livre, dont l’ambition est de mieux faire comprendre la technologie blockchain, d’en expliquer les avantages et les risques pour le monde industriel, les auteurs partagent leur expertise et leurs expériences et montrent l’ensemble des interactions positives entre blockchain et perform Yves-Michel Leporcher est expert digital blockchain et auteur de nombreux ouvrages dans le domaine.ances industrielles. Femme de l'industrie en 2014, Odile Panciaticia été à la tête de l’ingénierie Véhicule du groupe Renault. Elle est maintenant reconnue pour ses actions dans la transformation digitale et les projets blockchain.

]]> 2026-11-09T13:15:31 <![CDATA[ Python 3 : Build Reliable Programs from Core Syntax to Advanced Applications ]]> https://univ.scholarvox.com/catalog/book/88983571 Editeur : Packt Publishing

Develop practical Python skills from core syntax and data types to object-oriented programming and the standard library. Advance into debugging, networking, GUIs, Django, scientific computing, and project distribution.

Key Features

  • Broad coverage of core syntax, data types, the standard library, and advanced Python practices
  • Practical code examples that connect language concepts with professional development needs
  • A structured path from first programs to debugging, Django, data science, and project release

Book Description

Starting with the language’s foundations, readers learn how Python code is organized and executed, then practice control structures, files, functions, methods, and the data model. Clear examples explain numeric, sequential, mapping, set, collection, date, time, enumeration, and flag types, helping readers choose suitable structures and write with confidence. The focus then shifts to techniques for maintainable software. Readers work with modules, packages, object-oriented design, exceptions, generators, iterators, context managers, decorators, annotations, static type checking, and structural pattern matching. The standard library extends these skills into mathematics, logging, regular expressions, operating-system access, file systems, parallel work, data storage, networking, internet resources, email, debugging, quality assurance, and documentation. Later sections connect Python to real development through project distribution, virtual environments, alternative interpreters, graphical interfaces, Django, scientific computing, and data science. Historical context clarifies how the language evolved and why current practices matter. By the end of this journey, readers can move from core syntax to advanced applications with a practical reference for writing, testing, organizing, and extending effective Python programs.

What you will learn

  • Write Python programs using clear syntax and control flow
  • Apply core data types, collections, functions, and methods
  • Structure reusable code with modules, packages, and classes
  • Handle exceptions, iterators, decorators, and type annotations
  • Use the standard library for files, networks, logging, and data
  • Build advanced solutions with GUIs, Django, and scientific tools

Who this book is for

Beginners building a solid Python foundation and experienced programmers seeking a detailed working reference will benefit most. The material suits software developers, web developers, data and scientific computing practitioners, and professionals who want deeper skills in debugging, networking, GUIs, Django, packaging, quality assurance, and the standard library.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Graph Engineering — The Loop Memory Model : Give agentic workflows durable memory, traceability, and shared knowledge with graphs ]]> https://univ.scholarvox.com/catalog/book/88984022 Editeur : Packt Publishing

Give agentic systems a memory that survives every run by turning decisions, evidence, and execution history into durable, traceable graph structures

Key Features

  • Separate work history from durable knowledge with a two-graph architecture and clear bridge rules
  • Retrieve small, provenance tracked context windows from graph paths within token budgets
  • Run parallel workers with scoped promises, validated writes, reducer contracts, and hard budgets

Book Description

Agent loops are good at producing work, but the reasoning behind that work disappears the moment the context window closes. Two agents rediscover the same bug three sprints apart; a decision made in run 12 gets silently contradicted in run 47. Graph Engineering moves that reasoning out of transcripts and into an explicit knowledge graph. You'll model execution history separately from durable knowledge, then connect the two with a small set of bridge relationships. Markdown stays the authoritative source; the graph is a disposable index built for queries, not a second source of truth. From there, the book turns the model into a working mechanism: parse structured repository data, extract only genuine prose, resolve entities, and assemble typed relations. Retrieve small, relevant neighborhoods instead of dumping an entire knowledge base into context. See how evaluations check supporting paths, how concurrent workers publish changes without corrupting shared state, and how a pipeline preserves lineage from objective to artifact. The final section is judgment; which metrics reveal graph quality, how false merges and bad incentives produce confident errors, what this architecture costs, and when a simple ADR log or relational table is the better answer.

What you will learn

  • Choose the right architecture from loop, chain, swarm, DAG, or graph
  • Define separate schemas for work history and in-force knowledge
  • Turn Markdown nodes into a disposable query index
  • Add provenance, confidence, and validity to graph assertions
  • Compute NEAR links to surface duplicates without making them facts
  • Build a context assembler with typed, time-aware traversal
  • Check claims against graph paths instead of prose alone
  • Measure graph quality and know when not to build one

Who this book is for

This book is for senior software engineers, AI and agent engineers, software architects, and platform teams already experimenting with agent loops, coding agents, or multi-agent workflows. It is most useful when decisions, constraints, evidence, and execution history need to survive across runs or be shared between workers. Prior books in the Clarity series are helpful but not required; the manuscript recaps the lattice before extending it. Readers should be comfortable reasoning about software architecture and structured engineering workflows.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Python for AI, Machine Learning, and Deep Learning : Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning ]]> https://univ.scholarvox.com/catalog/book/88984024 Editeur : Packt Publishing

Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.

Key Features

  • Build Python, data science, mathematics, and statistics foundations for machine learning
  • Train, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost models
  • Create CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorch

Book Description

Learning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models. You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions. The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.

What you will learn

  • Use Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysis
  • Apply linear algebra, calculus, probability, and statistics to ML
  • Build regression, classification, and k-NN models
  • Engineer features and evaluate models with cross-validation and hyperparameter tuning
  • Train ensembles with random forests, XGBoost, LightGBM, and CatBoost
  • Build neural networks and CNNs with TensorFlow, Keras, and PyTorch
  • Develop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLP
  • Apply transfer learning and fine-tuning to computer vision and NLP

Who this book is for

This book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Mastering Graphics Programming With Vulkan : Develop a modern rendering engine featuring GPU-driven rendering, advanced lighting, and ray tracing ]]> https://univ.scholarvox.com/catalog/book/88984020 Editeur : Packt Publishing

Level up your graphics programming with Vulkan by building a modern renderer with GPU-driven techniques, ray tracing, advanced lighting, and neural materials Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Build a modern Vulkan renderer using a lightweight Vulkan accelerator framework, with bindless resources, frame graphs, and async GPU submission
  • Implement GPU-driven rendering with mesh shaders, GPU culling, and shadows
  • Advance from hardware ray tracing and ReSTIR GI to neural rendering with Slang
  • *Email sign-up and proof of purchase required

Book Description

Building a modern rendering engine can feel overwhelming when basic Vulkan tutorials only take you so far. This second edition bridges that gap, showing you how production-focused rendering systems manage resources, GPU work submission, geometry, and lighting, while incorporating techniques such as GPU-driven rendering, ray tracing, and ReSTIR GI. You’ll gain clarity and confidence working with the API thanks to a framework that strips away the boilerplate while preserving Vulkan’s concepts. As you progress, you’ll explore Vulkan features such as descriptor indexing, mesh shaders, and async compute for performance and flexibility. You’ll also use frame graphs to build a rendering architecture designed to scale and simplify resource management. From there, you’ll build GPU-driven rendering that leverages meshlets and GPU-based culling, then move into Vulkan ray tracing for shadows and reflections and implement ReSTIR GI. Finally, you’ll apply differentiable rendering with Slang to create neural materials. Written by two experienced graphics programmers, this book combines theory with implementation-focused guidance to help you gain practical skills. By the end, you’ll not only understand the Vulkan API at a deeper level but also have the knowledge to design and build your own modern renderer.

What you will learn

  • Manage GPU resources with bindless descriptors and shader reflection
  • Build a frame graph and coordinate asynchronous GPU workloads
  • Implement GPU-driven rendering with meshlets and mesh shaders
  • Render many lights and GPU-driven shadows efficiently
  • Implement volumetric fog, temporal anti-aliasing (TAA), and temporal upscaling
  • Build Vulkan acceleration structures and ray-tracing pipelines
  • Implement ray-traced shadows, reflections, and ReSTIR GI
  • Create neural materials with Slang and differentiable rendering

Who this book is for

Ideal for graphics programmers and engine developers who are already familiar with Vulkan or modern graphics APIs and want to deepen their understanding of real-time rendering architecture and system design. You should already be comfortable with C++, graphics fundamentals such as vectors and matrices, and core Vulkan concepts including pipelines, descriptors, command buffers, and GPU resource management. Motivated hobbyists and early-career developers can also benefit, provided they are comfortable with these core Vulkan concepts and ready to engage with more advanced architectural topics.

]]> 2026-11-04T08:00:01.283 <![CDATA[ AI Governance in Practice : Build responsible, audit-ready programs with ISO/IEC 42001, NIST AI RMF, and lifecycle controls ]]> https://univ.scholarvox.com/catalog/book/88983520 Editeur : Packt Publishing

Build an audit-ready AI governance program with practical assessments, lifecycle controls, and guidance aligned with ISO/IEC 42001, NIST AI RMF, OECD AI Principles, and the EU AI Act

Key Features

  • Apply ISO/IEC 42001, NIST AI RMF, OECD AI Principles, and the EU AI Act in practice
  • Conduct AI impact and risk assessments using structured methods and practical templates
  • Build audit-ready lifecycle controls, documentation, monitoring, and accountability
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Turn AI governance principles, standards, and regulatory requirements into a practical program that operates across the AI lifecycle. This book shows you how to build responsible AI governance around ISO/IEC 42001, the NIST AI RMF, OECD AI Principles, the EU AI Act, and other frameworks. You’ll conduct AI risk and impact assessments, define governance roles and decision rights, establish policies and lifecycle controls, and create documentation for transparency, accountability, and AI compliance. You’ll apply governance through development, deployment, monitoring, and retirement; address AI security and safety; prepare for AI incidents; and use AI audits and monitoring findings to strengthen controls. Practical scenarios, templates, checklists, and step-by-step guidance turn frameworks into repeatable organizational practices. Written by Hemang Doshi, who has more than 20 years of experience in system audit, IT risk and compliance, internal audit, risk management, information security audit, third-party risk management, and operational risk management, this book connects governance frameworks with practical implementation. By the end, you’ll be able to build an accountable, audit-ready AI governance program and integrate AI risk management with existing compliance, security, privacy, and risk processes.

What you will learn

  • Explain responsible AI principles and governance lifecycle risks
  • Compare ISO/IEC 42001, NIST AI RMF, OECD, and the EU AI Act
  • Conduct structured AI impact and risk assessments
  • Define accountable governance roles and decision rights
  • Create policies and lifecycle controls for responsible AI
  • Document AI systems for transparency and auditability
  • Prepare for AI incidents with structured response processes
  • Perform AI audits and improve governance from findings

Who this book is for

AI governance, compliance, and risk management professionals responsible for overseeing AI systems, evaluating AI risks, or implementing responsible AI practices, along with data scientists, ML engineers, internal auditors, privacy leaders, and technology executives. A foundational understanding of AI concepts, basic IT risk principles, and organizational policies or governance frameworks is recommended. This book also provides a strong foundation for AI GRC certifications such as AIGP, AAIA, AAIR, AAISM, AIMS, and others.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Embedded Linux Development Using Yocto Project : Build production-grade embedded Linux products with the Yocto Project 6.0 (Wrynose) ]]> https://univ.scholarvox.com/catalog/book/88983529 Editeur : Packt Publishing

Master the Yocto Project 6.0 (Wrynose) LTS and ship dependable embedded Linux products, from your first QEMU boot to GPL-compliant production builds backed by SBOMs, custom BSPs, and reproducible BitBake workflows Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Bootstrap reproducible builds in minutes with bitbake-setup and BitBake Configuration Fragments
  • Author custom layers, recipes, and distributions for production-grade embedded Linux
  • Generate SPDX 3.0 SBOMs, scan CVEs, and ship GPL-compliant releases with confidence

Book Description

The Yocto Project powers some of the world's most demanding embedded Linux products, from medical devices and industrial gateways to networking gear and consumer electronics. With this book, you join the engineers who rely on it every day to ship reliable, reproducible Linux systems. Embedded Linux Development Using Yocto Project, 4th Edition, is your hands-on guide to the Yocto Project 6.0 (Wrynose) LTS release. You'll move from your first QEMU boot to a custom production distribution running on real hardware, mastering Poky and BitBake along the way. The book covers custom layers and recipes, the Standard and Extensible SDKs, Toaster, package feeds, Build History, devshell debugging, and GPL compliance backed by SPDX 3.0 SBOMs and CVE scanning. A brand-new chapter on Managing BitBake Configuration walks through the bitbake-setup and bitbake-config-build workflows reshaping how teams bootstrap, share, and reproduce builds. You'll package BitBake Configuration Templates and Fragments, manage registries, and synchronize configurations across teams. By the end, you'll be ready to bake images for BeagleBone Black, Raspberry Pi 4 and 5, and VisionFive RISC-V boards, generate SBOMs ready for EU CRA and U.S. EO 14028 reporting, speed up iteration with QEMU, and lead embedded Linux projects with confidence. *Email sign-up and proof of purchase required

What you will learn

  • Bootstrap projects with bitbake-setup and BitBake Configuration Fragments
  • Drive builds from Toaster's web dashboard for full visibility
  • Assemble packages and host package feeds for production rollouts
  • Author custom layers, recipes, machines, and distributions
  • Build applications and the Linux kernel with standard and extensible SDKs
  • Pinpoint regressions with Build History, devshell, and Package Information
  • Boot custom images on BeagleBone Black, Raspberry Pi 4/5, and VisionFive
  • Generate SPDX 3.0 SBOMs, scan CVEs, and meet EU CRA reporting needs

Who this book is for

This book is for embedded Linux developers, integrators, and engineering leads who want to ship dependable products with the Yocto Project. If you build embedded Linux for a living, or you're tired of fragile build scripts and one-off Linux distributions, this book is your production playbook. You'll get the most value if you're comfortable with Git, the Linux command line, cross-compilation, and shell scripting. No prior Yocto Project experience is required. Hardware hackers graduating from prototypes to maintainable, reproducible products will feel right at home.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Advanced Smart Home Automation Solutions with Home Assistant : Elevate your home automation system with AI, Zigbee, MQTT, ESPHome, and Golioth ]]> https://univ.scholarvox.com/catalog/book/88983547 Editeur : Packt Publishing

Build scalable, secure IoT devices for your home automation system using advanced tools, protocols, and frameworks Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Master automation logic using YAML, Jinja2, Node-RED, and scripting in Home Assistant
  • Build custom ESP32 IoT devices, sensors, GPS modules, and cameras with ESPHome and MQTT
  • Secure and expand your smart home network using Zigbee, Golioth, and other best practices

Book Description

Advanced smart home setups often require more than standard configurations to achieve performance, security, and flexibility. This book helps electronics engineers and advanced home automation enthusiasts extend Home Assistant with deeper control over devices, networks, and integrations. You will begin with performance tuning and advanced YAML and Home Assistant Template configurations, then move on to secure network design using Zigbee and cloud integration with Golioth. You’ll build custom IoT devices with ESPHome, explore MQTT for communication, and develop ESP32-based touchscreen displays and camera systems. By the end of this book, you will also have covered advanced automation patterns using Node-RED and AppDaemon, along with AI-driven techniques to analyze user behavior and optimize automation workflows. Written by an expert with over 25 years of experience, this book provides practical methods to design, secure, and scale modern smart home systems. *Email sign-up and proof of purchase required

What you will learn

  • Implement complex YAML and Template automations in Home Assistant
  • Secure your home network using segmentation by creating VLANs and firewall rules
  • Deploy and manage Zigbee networks with Zigbee2MQTT and ZHA
  • Develop custom ESPHome firmware for sensors and touchscreen displays
  • Use MQTT tools to build and test device communication flows
  • Integrate Golioth Cloud IoT platform devices with Home Assistant
  • Create interactive dashboards and camera-based smart systems
  • Design AI-based automations to learn and suggest behaviors

Who this book is for

This book is for electronics and embedded engineers working with advanced sensors and automation, as well as IT and networking professionals securing and scaling smart homes using Home Assistant. It also suits IoT enthusiasts building custom setups. A working knowledge of Home Automation systems based on Home Assistant is recommended.

]]> 2026-11-04T08:00:01.283 <![CDATA[ La vertu du jeu vidéo ]]> https://univ.scholarvox.com/catalog/book/88983629 Editeur : Presses de l'Université du Québec

Les jeux vidéo font-ils bien plus que nous divertir?? Plusieurs penseurs, d’Aristote à ses héritiers, rappellent que l’épanouissement passe par la pratique de la vertu. Et si, à leur manière, les jeux vidéo pouvaient faire de nous des personnes vertueuses et, ultimement, épanouies??

Ce livre propose d’explorer la pleine valeur morale du jeu vidéo. Parmi la multitude de titres conçus pour nous divertir, certains vont plus loin?: ils invitent à réfléchir sur le sens de la vie et à faire preuve de sensibilité, de courage ou même d’humilité. Ces jeux «?édifiants?» nous encouragent à ralentir, à démêler nos émotions, à choisir avec soin – bref, à vivre une expérience qui dépasse l’écran en touchant aux fibres mêmes de notre être.

À partir d’exemples de jeux salués par la critique, La vertu du jeu vidéo montre comment les règles, les récits et l’esthétique des jeux vidéo orchestrent des expériences précieuses pour notre éducation morale. La pratique de la vertu étant le projet d’une vie, le jeu vidéo s’avère un terrain d’entraînement par excellence pour l’accueillir.

Adressé aux personnes curieuses, aux spécialistes du jeu ou à celles et ceux qui cherchent à comprendre la dimension humaine du média, cet ouvrage propose des repères accessibles et rappelle que le jeu vidéo peut aussi concourir à une vie harmonieuse.

]]> 2026-11-04T08:00:01.283 <![CDATA[ JavaScript : Build practical web, backend, and mobile applications with hands-on programming ]]> https://univ.scholarvox.com/catalog/book/88984019 Editeur : Packt Publishing

Develop JavaScript skills from core syntax to browser interactions. Put them to work in backend, mobile, and AI applications with step-by-step guidance.

Key Features

  • Broad language coverage connects JavaScript fundamentals with browser, server, and mobile work
  • Expanded methods, properties, and error coverage supports a professional development process
  • Sequential guidance links core concepts to dynamic web, backend, mobile, and AI applications

Book Description

Practical JavaScript development starts with a clear grasp of the language. This hands-on guide introduces values, data types, control flow, objects, and debugging, giving new readers a foundation and experienced programmers a structured refresher. Reference types and language features prepare you to work with code beyond isolated examples. Browser programming then connects those foundations to dynamic web pages. You work with events, forms, content updates, graphics, and web APIs before exploring object-oriented and functional programming, source code organization, and asynchronous behavior. Step-by-step instructions connect these concepts to web application development. The later material broadens your scope to backend applications with Node.js, mobile development with React Native, and AI applications such as local chatbots and image recognition with TensorFlow. Expanded treatment of methods, properties, and error types sits alongside guidance on a professional development process. By the end of this book, you will be equipped to apply JavaScript across browser, server, and mobile projects and explore its AI capabilities.

What you will learn

  • Write JavaScript using data types, control flow, and objects
  • Handle browser events, forms, and dynamic page content
  • Use web APIs to add browser functionality
  • Structure asynchronous code for web applications
  • Build server-side applications with Node.js
  • Create mobile applications with React Native

Who this book is for

Web developers working on frontend and backend applications, newcomers taking their first steps in JavaScript, and programmers refreshing their knowledge. Readers looking to extend their skills into server, mobile, and AI development will also benefit.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Enterprise AI Agent Security : A Practical Guide to MCP Security, Prompt Injection Defense, RAG Security, and Agentic AI Governance ]]> https://univ.scholarvox.com/catalog/book/88984026 Editeur : Packt Publishing

Secure enterprise AI agents with bounded autonomy, AgentSecOps, MCP security, RAG governance, identity controls, evals, policy, observability, audit evidence, and production-ready security patterns.

Key Features

  • Build secure AI agents with bounded autonomy and layered controls
  • Secure MCP, RAG, identity, tool calls, and agent permissions
  • Apply AgentSecOps, evals, observability, policy, and incident response

Book Description

AI agents can read sensitive data, call tools and APIs, write code, change system state, and act on behalf of users. Each action needs explicit authority and enforceable limits. Securing Enterprise AI Agents is a practical field guide to bounded AI autonomy. Learn to classify agent risk, assign ownership and approval paths, and design a seven-layer reference architecture. Secure Model Context Protocol (MCP) servers and tool calls, apply delegated identity and least privilege, and govern retrieval augmented generation (RAG) and enterprise knowledge. The book connects evaluations, observability and reliability controls with secure coding-agent workflows, policy as code, incident response and continuous AgentSecOps. Worked enterprise scenarios, exercises, illustrative code and a production-readiness checklist help engineering, security, risk and audit teams turn the guidance into controls they can assess. By the end, you will be able to define boundaries for agent actions, collect audit evidence, plan for failures and assess whether the controls justify production deployment.

What you will learn

  • Classify AI agents by authority, risk, and blast radius
  • Design secure seven-layer architectures for enterprise agents
  • Secure MCP servers, tool calls, and agent capability boundaries
  • Apply delegated identity, least privilege, and approval controls
  • Protect RAG pipelines from poisoning, leakage, and unsafe retrieval
  • Build evals, observability, and regression gates for agents
  • Enforce agent policies as code and prepare incident response
  • Implement continuous AgentSecOps and production readiness checks

Who this book is for

This book is for engineers moving AI agents into production, enterprise and security architects, and security, risk, audit and governance teams assessing agent deployments. AI platform leaders and technical decision-makers in regulated or high-impact environments will also benefit. Readers should understand application architecture APIs, cloud services, identity and access management, and basic LLM concepts. No particular agent framework is required.

]]> 2026-11-04T08:00:01.283 <![CDATA[ AI-Powered Financial Analysis : Automate reporting, forecasting, and turn financial data into actionable business insights ]]> https://univ.scholarvox.com/catalog/book/88984030 Editeur : Packt Publishing

Transform finance from a reactive reporting function into a forward-looking strategic partner using AI, automation, predictive insights, and Lean principles

Key Features

  • Automate routine finance work and redirect teams toward strategic analysis
  • Use AI for forecasting, budgeting, variance analysis, and risk sensing
  • Build an AI-ready finance function with Lean principles and governance

Book Description

Finance teams have more data than ever, yet much of their time is still consumed by reporting, manual processes, and explaining what has already happened. AI-Driven Finance shows how finance leaders can use AI, automation, and Lean thinking to shift from reactive reporting toward faster, forward-looking decision-making. Drawing on real-world examples and finance transformation experience, the book explores how to automate repetitive work, move beyond static dashboards, anticipate cost pressures, automate variance analysis, build risk-sensing dashboards, and make budgeting and capital planning more adaptive. You'll also learn how to create a Lean AI finance stack and improve alignment between finance and the wider business. Beyond technology, the book addresses the organizational changes required for successful AI adoption, including governance, ethics, team upskilling, cost visibility, and cross-functional alignment. A finance maturity roadmap helps you assess where your organization stands and identify how to move toward a more predictive, agile, and strategically valuable finance function.

What you will learn

  • Automate repetitive finance work and reduce reporting effort
  • Move from backward-looking reports to predictive insights
  • Improve cost control using predictive finance techniques
  • Automate variance analysis to uncover strategic insights
  • Build AI-augmented budgets and forecasts
  • Use risk-sensing dashboards to identify early warnings
  • Establish governance for responsible AI adoption
  • Build a roadmap for an AI-driven finance function

Who this book is for

This book is for CFOs, controllers, FP&A leaders, finance directors, analysts, and finance transformation professionals who want to move their teams beyond reporting toward strategic decision-making. It is also relevant to business leaders responsible for finance transformation and AI adoption. No data science background is required; the focus is on applying AI, automation, and Lean principles to finance processes and decisions.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Becoming a Salesforce Certified Technical Architect : Gain expert-led architecture mastery to pass the CTA board and lead large-scale complex programs ]]> https://univ.scholarvox.com/catalog/book/88983569 Editeur : Packt Publishing

Prepare for the CTA board with hands-on scenarios and expert guidance. Master Salesforce architecture, including Data Cloud, Hyperforce, and AI, and lead scalable programs with confidence. Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader.

Key Features

  • Prepare for the prestigious CTA review board with guidance from an experienced Salesforce CTA
  • Practice with realistic scenarios, solution presentations, and three complete mock reviews
  • Master Salesforce architecture across data, security, integration, governance, and communication

Book Description

Achieving the Salesforce CTA credential is the ultimate milestone for Salesforce professionals, and this book is your definitive roadmap to getting there. This updated edition equips you with the skills, structure, and confidence to design, present, and defend enterprise-scale solutions that meet real-world business challenges. Through realistic scenarios and hands-on guidance, this book helps you think, plan, and communicate like a CTA. Written by Tameem Bahri, a Salesforce Certified Technical Architect with more than 20 years of enterprise architecture experience and many years of CTA coaching, the book turns CTA expectations into a clear, repeatable method. Through realistic scenarios and three full mock reviews, you will learn to analyze ambiguous requirements, evaluate trade-offs, create architecture artifacts, and communicate an end-to-end solution under scrutiny. This edition introduces significant updates, including new guidance on Hyperforce, Data360, and AI concepts. It also includes a brand-new full mock scenario. You’ll explore modern patterns, governance automation, and data compliance in the context of evolving Salesforce technology. By the end of this book, you’ll be ready to face the CTA review board with a tested framework, practical artifacts, and the confidence to lead complex enterprise programs as a trusted Salesforce Technical Architect. Add-on: Online platform with Exam Tips and Flashcards

What you will learn

  • Understand the CTA review board format, process, and evaluation areas
  • Analyze ambiguous requirements and identify architecture drivers
  • Evaluate options, trade-offs, risks, and constraints
  • Design secure, scalable data, integration, and identity architectures
  • Create artifacts that communicate an end-to-end solution
  • Align governance, delivery, and release strategies
  • Explain and defend architecture decisions with clarity
  • Apply architecture practices through realistic scenarios
  • Prepare strategically for the CTA review board

Who this book is for

This book is primarily for experienced Salesforce architects, consultants, and technical leads preparing for the CTA review board or leading complex Salesforce programs. Software, solution, and enterprise architects outside the Salesforce ecosystem will also benefit from its transferable methods for requirements, trade-offs, secure and scalable design, governance, visualization, and decision communication. Examples and product choices are Salesforce-specific. CTA candidates should have substantial Salesforce implementation experience and at least three years in an architecture role.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Building AI-Powered Distributed Architectures with Aspire : Build cloud-native microservices with PostgreSQL, Redis, RabbitMQ, Keycloak, Ollama, and GenAI ]]> https://univ.scholarvox.com/catalog/book/88983587 Editeur : Packt Publishing

Build cloud-native distributed .NET applications with Aspire, ASP.NET Core microservices, generative AI, semantic search, and Azure deployment.

Key Features

  • Build an end-to-end distributed e-commerce system with AI capabilities
  • Integrate PostgreSQL, Redis, RabbitMQ, Keycloak, and Ollama with Aspire
  • Deploy secure, AI-enabled microservices to Azure Container Apps

Book Description

Build practical AI-powered distributed applications by seeing how microservices, messaging, security, data services, and generative AI work together in one project. This book guides you through building a cloud-native e-commerce application with independently deployed services, reliable communication, specialized data stores, and intelligent features. You’ll use Aspire to orchestrate Catalog and Basket microservices, configure service discovery, and integrate PostgreSQL and Redis. You’ll implement synchronous communication and asynchronous messaging with RabbitMQ and MassTransit, secure Basket APIs with Keycloak and JWT bearer tokens, and connect a Blazor frontend to the distributed backend. You’ll then add generative AI capabilities using Microsoft.Extensions.AI, Microsoft Agent Framework, and Ollama. You’ll build customer-support Q&A and semantic product search using embeddings and vector storage before deploying the application to Azure Container Apps. Written by software and solutions architect Mehmet Ozkaya, who has more than 15 years of industry experience and has taught more than 200,000 students, this book combines hands-on development with architectural guidance. By the end, you’ll be able to design, orchestrate, secure, enhance, and deploy AI-enabled distributed .NET applications.

What you will learn

  • Design cloud-native distributed systems with Aspire
  • Build Catalog and Basket microservices with ASP.NET Core
  • Orchestrate PostgreSQL and Redis as backing services
  • Implement event-driven messaging with RabbitMQ and MassTransit
  • Secure microservice endpoints with Keycloak and JWT bearer tokens
  • Build enterprise AI features with Microsoft.Extensions.AI, Ollama, and Microsoft Agent Framework
  • Implement semantic search with embeddings and vector storage
  • Deploy distributed applications to Azure Container Apps

Who this book is for

.NET developers, software engineers, and solution architects who want to build cloud-native distributed applications and add generative AI capabilities will benefit from this book. It is also suited to developers exploring microservices, event-driven architecture, Aspire, and AI integration. Basic knowledge of C# and programming concepts is required.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Automated Code Refactoring and Modernization with OpenRewrite : Build reusable recipes for scalable migrations, technical debt reduction, and code transformation ]]> https://univ.scholarvox.com/catalog/book/88983619 Editeur : Packt Publishing

Automate repetitive code refactoring and migrations with reusable recipes that make large-scale code changes faster, safer, and more consistent.

Key Features

  • Automate repetitive refactoring and migrations across codebases
  • Create declarative, Refaster template, and imperative recipes
  • Apply custom transformations through your existing build pipeline
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Automate repetitive refactoring and make large-scale code changes consistent and scalable with OpenRewrite. This book shows you how to use existing recipes and create your own transformations, helping you modernize codebases while significantly reducing the time and errors associated with manual changes. You’ll begin with lossless semantic trees (LSTs), recipes, visitors, and styles to understand how OpenRewrite represents and transforms source code. You’ll then learn to find, configure, compose, and run recipes, add recipe sources, and integrate transformations with your build pipeline. Next, you’ll create declarative recipes and Refaster templates before progressing to imperative recipes. You’ll work with visitors, cursors, shared data, non-isomorphic transformations, configuration options, testing, validation, and multiple visitors. All examples are visualized using Java as a programming language. Written by freelance software engineer Ansgar Schulte, who brings many years of project experience and has taught more than 15,000 students, this book connects OpenRewrite concepts with techniques you can apply to real projects. By the end, you’ll be able to automate recurring refactoring tasks and build reusable recipes for your own codebases.

What you will learn

  • Understand recipes, visitors, styles, and LSTs
  • Find, configure, compose, and run OpenRewrite recipes
  • Add and use new recipe sources in your projects
  • Create declarative and Refaster template recipes
  • Build imperative recipes with visitors and cursors
  • Test, validate, and configure custom recipes
  • Understand isomorphic and non-isomorphic visitors
  • Implement complex transformations with multiple visitors
  • Integrate automated refactoring into build pipelines

Who this book is for

Software developers and solution architects who spend significant time on manual refactoring, migrations, or technical debt reduction will benefit from this book. You should understand Java syntax and have access to a Java development environment with a JDK, IDE, and build tool. No previous OpenRewrite knowledge or refactoring experience is required.

]]> 2026-11-04T08:00:01.283 <![CDATA[ Autodesk Revit for Beginners : Build BIM models, documentation, and visualizations through a complete home project ]]> https://univ.scholarvox.com/catalog/book/88983521 Editeur : Packt Publishing

Build the practical Revit skills needed to create professional BIM models, drawings, schedules, and visualizations for digital construction projects

Key Features

  • Build BIM skills through a complete residential project
  • Create professional drawings, schedules, and 3D models
  • Learn industry workflows used across architecture, engineering, and construction

Book Description

Build practical Revit 2026 skills through a complete home project, progressing from initial setup to professional BIM models, drawings, schedules, and visualizations. Guided by Gary Cowan, a BIM and VDC specialist with 20 years of experience who has been recognized as an Autodesk Expert Elite member, you’ll learn how these outputs come together in an industry-focused workflow. You'll use your own home as the main project, with focused exercises to explore additional Revit workflows. Using this familiar building as your starting point, you'll learn how to set up a Revit model, create architectural, structural, and MEP elements, manage project information, and produce coordinated documentation. Along the way, you'll explore families, parameters, materials, worksharing, schedules, sheets, and industry-standard BIM workflows used across the AEC industry. You'll also learn how to create plans, sections, elevations, and 3D views, use linked models for coordination, and export models to IFC and NWC files. The book concludes with an introduction to visualization, interoperability, and XR workflows that extend the value of your BIM models beyond Revit. By the end of this book, you'll have developed a project you can build on for your portfolio and gained the confidence to continue your journey in digital construction using Revit 2026.

What you will learn

  • Understand digital construction and Revit's role in the industry
  • Gain confidence in navigating and using Revit's key functions
  • Model walls, floors, stairs, roofs, and other building elements
  • Learn worksharing, linked models, exports, and file management
  • Create custom parametric families and apply materials
  • Control 2D and 3D views consistently with view templates
  • Create sheets, manage revisions, print PDFs, and export DWGs
  • Export IFC and NWC files and explore XR workflows

Who this book is for

Architects, engineers, and drafters at the beginning of their careers who want to learn foundational Revit 2026 skills for digital construction. No previous knowledge or experience with Revit or other 2D/3D software is required, although basic knowledge of construction drafting or 3D modeling will be beneficial.

]]> 2026-11-04T08:00:01.283