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Bushcraft: Complete Survival Guide - Master outdoor skills and techniques for living Off-Grid, overcoming challenges and thriving in wild and extreme environments
English | 13 May 2025 | ASIN: B0D7CW2Y8F | 169 pages | Epub | 7.28 MB
Have you ever dreamed of mastering the ancient techniques of bushcraft that would allow you to survive and thrive in the wild? If you yearn to become a survival expert, capable of facing any challenge in extreme environments, this guide is your passport to self-sufficiency.With essential survival skills, wildlife techniques and outdoor projects, Bushcraft is your indispensable manual for reconnecting with nature and becoming a wildlife expert. This book will prepare you to face critical emergencies and help you forge an unbreakable connection with the natural world.Why is bushcraft so important?A study by the University of Montana revealed that 93% of successful survival cases were based on the mastery of basic bushcraft techniques.According to the US National Park Service, mastery of bushcraft techniques reduces the chances of serious injury in the wild by 75%.A survey of 500 experienced hikers showed that 87% considered craft skills to be "essential" for safety in the wild.Forget the myths spread by reality shows. This book reveals real survival techniques that can save your life.Discover the essential skills for survival in the wild:Revolutionary techniques for adapting to and thriving in the most challenging wilderness environments.Ancestral secrets for building solid shelters using only natural materials.Proven methods for locating and purifying water in the wild.Crucial skills for starting and maintaining a fire that will save your life in extreme conditions.Hunting, fishing and gathering strategies for becoming a natural predator.How to forge powerful tools from the resources around you.Navigation tactics to find your way in the most treacherous areas.First aid techniques that could mean the difference between life and deathWith detailed instructions and expert advice, this book will turn you into a bushcraft master, capable of surviving in any situation.No high-tech equipment or years of training are needed. Bushcraft teaches you to be self-sufficient with minimal resources.If you are ready to master survival, bushcraft and life in the wild, and join the elite of survival experts, then this book is your gateway to a new life.Don't wait any longer, start your journey towards se

Burgenland für Entdecker: Schotti to go
Deutsch | 16 Sept. 2021 | ISBN: 3990502093 | 220 Seiten | EPUB (True) | 8.97 MB
100 Jahre Burgenland - Grund genug für Reisephilosoph Michael Schottenberg, Österreichs jüngstem Bundesland einen Besuch abzustatten. Mit seiner roten Vespa braust er von Kittsee bis zum Csaterberg, von Stinatz bis Andau, macht halt auf Burgen und Kulturbühnen, in Stadtschlaining wie in Bildein. Die junge Dame aus den Golden Twenties ist der Mittelpunkt der Welt, ist doch das Schicksal von Österreichern, Kroaten, Ungarn und Roma eng mit ihr verknüpft. Der ethnischen Vielfalt und einzigartigen Kultur der Region begegnet Schotti in Gesprächen mit außergewöhnlichen Menschen: dem Gschalerma(n)dlbauer in Heiligenbrunn, einem Töpfermeister aus Stoob, dem Grabinschriftenjäger von Eisenstadt oder dem, der mit den Düften tanzt, in Frauenkirchen. Entstanden ist ein humorvolles, geistreiches Buch für Entdecker - und ein Geburtstagsgeschenk der besonderen Art für ein besonderes Land.

Bulle-und-Bär-Strategie: Die Börse ist keine Einbahnstraße!
Deutsch | 10 Jan. 2025 | ASIN: B0DSPWPXHS | 94 Seiten | EPUB (True) | 3.42 MB
Die ,,Bulle-und-Bär-Strategie" ist eine umfassende Handelsmethode, die speziell entwickelt wurde, um sowohl von steigenden als auch von fallenden Kursen am Finanzmarkt zu profitieren. Das Buch führt Leser Schritt für Schritt durch die Grundlagen und Anwendungen der Strategie, basierend auf der Analyse des S&P 500.Mit einer klar strukturierten Vier-Fenster-Methode auf der Handelsplattform MetaTrader 5 wird gezeigt, wie technische Indikatoren, Buy-Stop- und Sell-Stop-Orders sowie Fibonacci-Retracements effektiv eingesetzt werden können. Neben praktischen Beispielen bietet das Buch wertvolle Einblicke in das Risikomanagement, die Psychologie des Tradens und steuerliche sowie rechtliche Aspekte des CFD-Handels.Diese Strategie kombiniert fundiertes Wissen mit praxisnahen Anleitungen und richtet sich sowohl an Einsteiger als auch an erfahrene Trader, die ihre Handelsansätze diversifizieren möchten. Ein unverzichtbarer Leitfaden für alle, die die Märkte besser verstehen und erfolgreicher handeln möchten.

Building the Economic Value Architecture with SAP & Agentic AI - Executive Edition: Connecting Intelligence, Decisions, and Execution to Drive Measurable Economic Value (The AI Revolution)
English | May 8, 2026 | ASIN: B0H11LTH37 | 356 pages | Epub | 1.48 MB
Building the Economic Value Architecture with SAP & Agentic AIConnecting Intelligence, Decisions, and Execution to Drive Measurable Economic ValueAre you investing in AI pilots, copilots, and assistants without seeing a clear connection to enterprise financial performance?Artificial intelligence has entered the enterprise with enormous force, but also with enormous confusion. Many organizations still treat AI as a collection of isolated use cases: chatbots, writing assistants, search layers, and productivity tools placed on top of existing work. Yet enterprises do not create value through use cases. They create value through recurring operational decisions that influence revenue, cost, margin, capital, and risk.Building the Economic Value Architecture with SAP & Agentic AI introduces the Economic Value Architecture, or EVA, as a rigorous decision-and-execution operating model for connecting AI reasoning with enterprise action and measurable financial impact. SAP environments are used as the concrete enterprise setting because they are where business processes, systems of record, execution authority, and financial consequences converge.This is not a book about AI hype. It is not a technical configuration guide. Nor is it a book about SAP technologies. It is a strategic and architectural framework for leaders who need to understand how Agentic AI can move from isolated analysis toward governed execution without violating the structural laws of the SAP enterprise landscape.Inside, you will learn how to:Escape pilot purgatory by shifting the focus from abstract AI experiments to the Decision Object: a structured representation of a decision that makes financial consequence visible before action is taken.Govern the AI bottom line through the Shadow AI P/L, a mechanism for comparing expected financial impact before execution with realized outcomes after execution, while accounting for the dynamic cost of machine execution.Enforce architectural discipline by separating the AI control plane from the execution plane, preserving the SAP S/4HANA Clean Core, and using governed interfaces, RAG, and MCP to connect reasoning safely with enterprise reality.Reclaim managerial leverage by understanding why human confirmation is not a failure of autonomy, but a necessary control valve for risk containment, legitimacy, and accountability. The machine prepares the decision; the human retains consequential authority.Deploy the Hidden Value Study as a strict commissioning method for identifying, validating, and prioritizing economically justified Decision Objects before implementation begins.Written for executives, enterprise architects, CFOs, COOs, transformation leaders, SAP decision-makers, and senior consultants, this book strips away vendor marketing and technology spectacle. It argues that the future of enterprise AI should not be organized around where a model can be inserted, but around where decisions determine value.The central question is no longer whether AI can transform the enterprise.The question is whether the enterprise is disciplined enough to convert intelligence into economically accountable execution.

Building RAG Applications: A Hands-On Guide to Retrieval-Augmented Generation with Python (The Practical Tech Guide Series)
English | May 18, 2026 | ASIN: B0GX2V585J | 148 pages | Epub | 1.59 MB
Build practical RAG applications without getting lost in vendor noise.Retrieval-Augmented Generation, or RAG, is one of the most useful patterns for building AI applications that answer from real documents instead of unsupported guesses. But many explanations jump too quickly into tools, vector databases, and frameworks before explaining how the system actually works.This book takes a practical, project-first approach.You will build docs_rag_app, a small local RAG application in Python that answers questions over a folder of documents. Step by step, you will learn how to load documents, split them into chunks, retrieve relevant evidence, generate grounded answers, add citations, evaluate quality, and debug bad results.Along the way, the book explains the ideas behind the code:why LLMs need retrievalhow RAG differs from fine-tuning, long context, memory, and tool callshow chunking affects answer qualityhow lexical and vector retrieval workwhy reranking improves difficult caseshow citations and abstention reduce hallucination riskhow to evaluate and refresh a RAG systemwhy retrieved text should be treated as evidence, not trusted instructionsThe goal is not to build the largest possible AI system. The goal is to build a small RAG application that is easy to inspect, easy to debug, and strong enough to improve.If you can read basic Python and want to understand how retrieval changes AI application design, this guide gives you a clear path from first working prototype to practical local RAG app.

Building Production RAG Systems: Vector Databases, Embeddings, and Retrieval Engineering (Production AI Engineering Series)
English | May 18, 2026 | ASIN: B0H25PTWWL | 87 pages | PDF | 28.41 MB
Build Enterprise RAG Systems That Actually Work in ProductionRetrieval-Augmented Generation (RAG) is the definitive architecture for enterprise AI, yet transitioning from a simple prototype to a reliable production deployment remains a massive engineering challenge. Building Production RAG Systems is the definitive guide for software engineers and AI developers looking to bridge the gap between a proof-of-concept and a system trusted by thousands of daily users.Rather than rehashing basic prompt engineering, this comprehensive technical resource dives deep into the complex architecture, system design, and retrieval engineering required to scale AI applications. You will learn to eliminate hallucinations, dramatically improve answer quality, minimize query latency, and prevent runaway API costs.Inside this book, you will master:Advanced Chunking Strategies: Implement fixed-size, semantic, recursive, and late-chunking approaches for optimal context preservation.Vector Database Selection: Choose the right engine for your workload, including deep dives into Pinecone v3, Weaviate, ChromaDB, and pgvector.Hybrid Search & Reranking: Combine dense retrieval with BM25 sparse search, and build reranking pipelines using cross-encoders and ColBERT v2.Robust Evaluation Pipelines: Move beyond intuition and evaluate RAG quality using RAGAS, TruLens, and custom metrics.Cost Optimization & Reliability: Tune approximate nearest-neighbor search, implement circuit breakers, caching, and fallback strategies.Written specifically for engineers who understand AI fundamentals and need production-grade architectural patterns. Stop struggling with brittle generative AI deployments and start building highly reliable, cost-effective, and scalable enterprise retrieval systems today.

Building LLM Applications Without the Fog: A Practical Guide to RAG, Embeddings, Vector Databases, Fine-Tuning, Evaluation, and AI-Powered Apps (LLMs Without the Fog Book 2)
English | May 10, 2026 | ASIN: B0H1737GFT | 438 pages | Epub | 277.20 KB
Everyone is using AI. Far fewer people know how to build useful applications with it.Large Language Models can write, summarize, classify, code, and analyze. But building an AI-powered application that real users can depend on requires more than a prompt.A good LLM application is a system.It needs APIs, prompt templates, structured outputs, embeddings, vector databases, retrieval, evaluation, error handling, privacy awareness, and practical product design.Building LLM Applications Without the Fog is the second book in the LLMs Without the Fog series - a practical guide for developers, students, technical founders, data analysts, software engineers, and builders who want to move from understanding LLMs to creating real AI-powered applications.This is the builder book.Before you move into production AI, advanced agents, LLMOps, security, deployment, or an LLM engineering career, you need to understand how LLM applications are actually designed and built.Inside, you will learn:What it really means to build with LLMsWhy an LLM application is more than a chatbotHow an LLM-powered app is structuredHow software connects to model APIsHow prompt templates improve consistency and reuseHow structured outputs turn responses into usable dataWhat embeddings are and why they make meaning searchableHow vector databases support semantic searchWhat RAG is and why it mattersHow to connect LLMs to documents and knowledge basesWhy RAG reduces hallucination but does not eliminate itWhen to use prompting, RAG, fine-tuning, tools, or human reviewWhy evaluation matters more than impressive demosHow to recognize common LLM application failure modesHow to move from prototype to practical AI productThis book is not about training frontier models from scratch.It is about building intelligently with existing models.By the end, you will understand that useful AI applications are not created by simply "adding AI" to a product. They are designed around real users, real data, real workflows, and real limitations.Building LLM Applications Without the Fog continues the complete series:Book 1 - LLMs Without the FogBuild your foundation and understand Large Language Models clearly.Book 2 - Building LLM Applications Without the FogLearn how real LLM-powered applications are built with APIs, embeddings, vector databases, RAG, evaluation, and practical app design.Book 3 - Production LLMs Without the FogUnderstand how serious AI systems are deployed, secured, monitored, operated, and improved.Book 4 - Becoming an LLM Engineer Without the FogFollow a practical roadmap to build skills, create portfolio projects, and move toward opportunities in the AI field.If Book 1 helped you understand LLMs, this book helps you build with them.Move from understanding to building. Create useful LLM applications without the fog.

Building Intelligent Agents: From First Principles to Android, Multi-Agent Systems, and Production Autonomy (Artificial Intelligence)
English | June 1, 2026 | ASIN: B0H3K2MH75 | 1004 pages | Epub | 47.74 MB
Building Intelligent Agents: From First Principles to Android, Multi-Agent Systems, and Production AutonomyThere is a fashionable confusion abroad - that an intelligent agent is merely a chatbot wearing a more impressive coat - and this book sets out, with some deliberation, to dispel it. An agent observes, remembers, decides, acts, examines the consequences of its action, and proceeds under constraint; the distinction is easy to state and notoriously difficult to honour, which is precisely why the matter deserves a book rather than a pamphlet.Written for the reader who has been promised everything and taught nothing, this volume assumes no prior familiarity with the terminal, the model, the application store, or even the word agent itself. It defines its symbols before it uses them, derives the agentic loop rather than asserting it, and insists throughout that the reader arrive at something usable: a configured agent, a grounded knowledge system, a safe approval gate, a mobile assistant, a deployable product. The emphasis falls upon no-code and minimal-code construction, not because code is despised, but because code ought never to become the price of admission.What distinguishes the work is its commercial sobriety. Android, Windows, macOS, and the web are treated here as legitimate destinations for genuine products, and so the book attends to publishing, billing, review, permissions, privacy, and support with the same seriousness it brings to prompts, memory, retrieval, tools, and orchestration. Across fifty-nine chapters and more than six hundred pages it climbs a complete ladder - foundations, platforms, multi-agent coordination, security, observability, five worked capstones, and a full reference system - from the first rung to professional design.Assume nothing. Build something. Sell it honestly.

Building Conversational AI with LLMs and Agents: From the mathematics of attention to production agent systems. A practitioner's guide for engineers, researchers, and students
English | May 13, 2026 | ASIN: B0H1MQH23D | 5372 Pages | Epub | 71.13 MB
Build Production AI Agents from First PrinciplesThe 39-chapter LLM textbook that takes you from PyTorch fundamentals to shipping autonomous agents. 800+ pages of theory, code, and production engineering, written for engineers, researchers, and students who actually need to ship working LLM systems.If you have spent the last year trying to learn LLMs from scattered blog posts, GitHub READMEs, and outdated tutorials, this book is for you. Every chapter answers a specific question someone shipping LLM-powered systems has actually had to answer: how attention works, how to fine-tune a model on your data, how to build an agent that uses tools without going off the rails, and how to evaluate the thing you just built.What you will learnFoundations (Chapters 0-5): PyTorch and ML essentials, NLP and text representation, tokenization, sequence models, the Transformer architecture from scratch, decoding strategies.Understanding LLMs (Chapters 6-9, 18): pre-training, scaling laws, the modern model landscape, reasoning and test-time compute, inference optimization, and mechanistic interpretability.Working with LLMs (Chapters 10-12): APIs, prompt engineering, hybrid ML+LLM architectures.Training and adaptation (Chapters 13-17): synthetic data generation, fine-tuning, PEFT (LoRA, QLoRA), distillation, model merging, alignment with RLHF and DPO.Retrieval and conversation (Chapters 19-21): embeddings, vector databases, semantic search, retrieval-augmented generation, conversational AI systems.Agentic AI (Chapters 22-26): agent foundations, tool use and function calling, multi-agent systems, specialized agents, agent safety in production.Multimodal and applications (Chapters 27-28): vision-language models and real-world LLM applications.Production (Chapters 29-31): evaluation, observability, monitoring, engineering for LLM systems at scale.Safety and strategy (Chapters 32-33): alignment, ethics, regulation, product strategy, and ROI.Frontiers (Chapters 34-35): emerging architectures, the broader impact of AI on society.From idea to product (Chapters 36-38): shipping, scaling, and steering LLM-powered products.What makes this book differentTwenty tailored reading pathways. Whether you're a RAG engineer, agent builder, ML-to-LLM transitioner, product leader, or instructor, the book has a curated path through the chapters most relevant to your goal.Course-ready. Nine syllabi are included for graduate, undergraduate, and specialist tracks. Instructor copies and slide decks are available for course adoption.22 framework appendices covering Hugging Face, LangChain, LangGraph, CrewAI, LlamaIndex, Semantic Kernel, DSPy, plus environment setup, distributed ML, inference serving, and Docker.Up to date for 2026. Reasoning models, test-time compute, modern PEFT, agentic protocols (MCP, A2A), and multimodal foundation models all covered.Every chapter has a hands-on lab. You learn by building, not just by reading.

Building Complex Multi-Agent Systems Using Pattern Prompting: A guide to building robust and secure GenAI applications using software engineering best practices
English | May 15, 2026 | ASIN: B0GZW71GJH | 451 pages | Epub (True) | 33.05 MB
Learn how to build GenAI applications using proven patterns and abstractions to work confidently across tools and frameworksFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesLearn core abstractions and enterprise patterns behind modern GenAI systemsBuild secure, scalable agentic architectures using familiar engineering toolsGenerate multi-agent workflows and RabbitMQ-based GenAI configurationsUse our Topologos prompt to easily build and test any agentic systemBook DescriptionLearn how to build GenAI applications using proven software engineering patterns instead of rapidly changing frameworks. This book helps engineers build secure, scalable agentic systems with familiar tools and practical, engineer-to-engineer architectural guidance.You will connect GenAI concepts such as agentic workflows, embeddings, and vector databases to enterprise patterns, including components, adapters, and microarchitectures. Established GoF and enterprise design patterns help explain agentic behavior and system design, enabling you to reason about architecture rather than memorize tools. The book also shows you how to generate multi-agent and GenAI patterns as RabbitMQ configurations for scalable orchestration and communication.Using language-agnostic examples and widely used messaging, orchestration, and data technologies, you will build production-ready systems that integrate with existing infrastructure without unnecessary complexity. You will also use our Topologos prompt to build, modify, and test deploy-ready multi-agent systems quickly while improving robustness and maintainability.By the end of this book, you will be able to design reliable GenAI systems, make informed architectural decisions, and adapt confidently as tools and frameworks evolve.*Email sign-up and proof of purchase requiredWhat you will learnBuild GenAI applications that are secure, reliable, and scalableApply enterprise patterns to architect reliable GenAI systemsUnderstand the core concepts behind LLMs needed for application developmentUse practical engineering approaches for agentic architecture designGenerate RabbitMQ configurations for multi-agent GenAI systemsBuild and test robust multi-agent systems using our Topologos promptWho this book is forThis book is written for experienced software engineers, architects, and technical leads who want to add GenAI to their toolbox without abandoning the engineering principles that define good software.It assumes familiarity with basic programming and software development, but does not require prior experience with AI, machine learning, RabbitMQ, or Airbyte. All necessary concepts and tools are introduced in contexts.Table of ContentsIntroduction: Patterns, Abstractions, and the GenAI LandscapeEmbeddings: The Language of AIBuilding with GenAI: Parameters, Tuning, and Project PhasesBuilding Your First RAG AppStarting Your Data Migration ProjectIngesting Data Using Airbyte and PineconeTips and Best PracticesPattern-Guided Coding: Using Patterns as the Design Vocabulary for GenAI Systems Built on RabbitMQImplementing the ReAct Pattern Over RabbitMQThe Future and Limitations of LLMsAppendix A: Pattern Reference: GoF, Enterprise Integration, Reliability, and GenAI Microarchitecture PatternsAppendix B: Topologos User Manual