richa publishing minds (187 resultados)

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Paperback. Condición: new. Paperback. Build websites the modern way: faster, smarter, and with the power of AI integrated into your workflow.Modern HTML & CSS with AI is the first practical guide that combines timeless front-end fundamentals with intelligent tools that developers use today. Whether you are beginning your coding journey or upgrading your professional skills, this book gives you everything you need to build modern, responsive, and beautifully structured websites with clarity and confidence.Why This Book Stands OutUnlike traditional HTML and CSS books that rely heavily on memorization and outdated workflows, this guide teaches you how front-end development truly works in 2025 and beyond. You will learn real reasoning, real patterns, and real-world implementation enhanced by responsible AI collaboration.This book does not replace understanding with shortcuts. Instead, it shows you how to combine human creativity with AI-supported efficiency to craft clean, scalable, and intelligent websites.What You Will Gain- A strong foundation in semantic, accessible, modern HTML structure- Mastery of CSS layout systems including Flexbox, Grid, and container queries- Professional-level design system concepts: variables, tokens, spacing, typography, theming- The ability to use AI for code generation, refactoring, debugging, and workflow acceleration- Confidence in building responsive, fast, and polished interfaces for real-world use- Three full end-to-end projects that integrate human decision-making with AI assistance- A future-ready mindset for staying relevant as the web evolvesEach chapter blends fundamental skills with modern examples, diagrams, and AI-enhanced techniques, ensuring you learn both the "how" and the "why" of front-end development.Who This Book Is For- Beginners who want a clear, modern path- Self-taught developers who need structure and professional guidance- Designers moving into front-end- Students preparing for real-world workflows- Working professionals upgrading their process through AI- Anyone who wants to build intelligent, responsive, accessible websitesIf you want to understand not just how to write code, but how to think like a developer in the AI era, this book is for you.What This Book Includes- Modern HTML and CSS fundamentals, explained clearly- AI workflows for writing, reviewing, and improving code- Semantic architecture and SEO-friendly structure- Responsive layout mastery with Flexbox, Grid, and container queries- Theming and design systems for scalable UIs- Modern animation principles and micro-interactions- Debugging, accessibility testing, and performance optimization- Deployment to GitHub Pages, Netlify, and Cloudflare- Three complete real-world projects- Appendices with prompts, accessibility checklists, debugging tools, and terminologyAbout the AuthorLing Tianhao is a front-end developer, educator, and technology writer known for making modern web development both approachable and future-ready. His work focuses on combining timeless fundamentals with intelligent, AI-supported workflows that help developers build efficiently without sacrificing quality or creativity.Your Next StepIf you want a guide that teaches HTML, CSS, and the intelligent workflows of the future, Modern HTML & CSS with AI</st Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condición: As New. Unread book in perfect condition.

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Paperback. Condición: new. Paperback. Building LLM Agents with RAG, Knowledge Graphs & ReflectionA Practical Guide to Building Intelligent, Context-Aware, and Self-Improving AI AgentsBy Mira S. DevlinTransform Large Language Models into Intelligent Agents That Reason, Retrieve, and ReflectLarge language models can generate text-but intelligence requires more than words.True intelligence demands reasoning, memory, and reflection. It requires systems that can connect what they know, retrieve what they need, and learn from what they produce.In Building LLM Agents with RAG, Knowledge Graphs & Reflection, AI systems architect Mira S. Devlin guides you beyond the surface of generative AI into the world of agentic intelligence-where LLMs evolve from reactive tools into dynamic collaborators capable of grounding responses in truth, understanding context, and improving over time.This book doesn't just explain concepts-it helps you build them. Each chapter blends theory, diagrams, and applied examples to show how retrieval, reasoning, and reflection interact inside modern AI agents. Whether you're constructing a self-updating research assistant or a multi-agent workflow, you'll gain a deep understanding of how today's most advanced cognitive systems are designed.What You'll LearnThe Cognitive Core of AI AgentsUnderstand the architecture of transformers, tokenization, and attention.Explore the shift from static LLMs to adaptive, outcome-driven agents.Learn how retrieval, reflection, and reasoning form the four pillars of intelligence.Retrieval-Augmented Generation (RAG)Master the techniques that make models factually grounded and transparent.Implement retrievers, rankers, and generators using open-source frameworks.Evaluate accuracy with metrics like RecallatK, PrecisionatK, and grounding quality.Knowledge Graphs and Structured ReasoningDesign and query graph-based knowledge systems using Neo4j, ArangoDB, or GraphRAG.Combine structured knowledge with unstructured language for explainable AI.Reflection and Cognitive LoopsBuild agents that evaluate their own outputs and correct themselves.Implement Plan Act Reflect Revise cycles for self-improving intelligence.Explore short-term and long-term memory systems for continuous learning.Multi-Agent CollaborationUse frameworks like CrewAI, LangGraph, and AutoGPT2 to orchestrate coordination.Key FeaturesEnd-to-end coverage: From LLM fundamentals to advanced RAG and reflection architectures.Practical code labs: Step-by-step walkthroughs in Python with modular components.Visual clarity: Concept diagrams, data flow maps, and evaluation schematics throughout.Debugging insights: Identify hallucinations, reasoning gaps, and retrieval errors with real-world examples.Scalable design patterns: Extend single-agent models into multi-agent collaborative systems. This book is written for: AI developers, data scientists, and engineers who want to move beyond simple LLM prompts.Architects and product innovators building intelligent, explainable, and adaptive AI systems.Researchers and students seeking a structured understanding of retrieval-based reasoning and reflection.Tech leaders and educators integrating agentic AI into enterprise or academic environments.You don't need a supercomputer-just intermediate Python skills, a working knowledge of APIs, and curiosity. Every example can be run on a standard laptop or cloud environment Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condición: new. Paperback. Large language models now sit at the core of modern software systems. They power search, recommendation engines, coding assistants, conversational interfaces, and autonomous agents. Yet for many engineers and practitioners, these models remain opaque-understood through fragments of code, borrowed recipes, or surface-level explanations.This book was written to change that.Modern Large Language Models is a clear, systems-level guide to understanding how transformer-based language models actually work-starting from first principles and building upward toward complete, modern LLM systems.Rather than treating large language models as black boxes, this book explains the fundamental ideas that make them possible: probabilistic language modeling, vector representations, attention mechanisms, optimization, and architectural composition. Concepts are introduced gradually, with visual intuition and concrete reasoning before full implementations, allowing readers to develop understanding that transfers beyond any single framework or model version.The book takes you from the foundations of language modeling to the realities of training, fine-tuning, evaluation, and deployment. Along the way, it connects theory to practice, showing how design decisions shape model behavior, performance, and limitations.This is not a collection of shortcuts or prompt recipes. It is a guide for readers who want to reason about large language models as engineered systems-systems that can be analyzed, debugged, improved, and deployed with confidence.What You'll Learn- How language modeling works at a probabilistic level-and why it matters- How tokens, embeddings, and vector spaces encode meaning- How self-attention and transformer architectures operate internally- How complete GPT-style models are built from first principles- How training pipelines work, including optimization and scaling considerations- How fine-tuning, instruction tuning, and preference optimization fit together- How embeddings, retrieval, and RAG systems extend model capabilities- How modern LLM systems are evaluated, deployed, and monitored responsiblyWhat Makes This Book DifferentMost books on large language models focus either on high-level descriptions or narrow implementation details. This book takes a first-principles, systems-oriented approach, emphasizing understanding over memorization and architecture over tools.The examples use PyTorch for clarity, but the ideas are framework-agnostic and designed to remain relevant as tooling and architectures evolve. Clean diagrams, structured explanations, and carefully reasoned trade-offs replace hype and jargon.Who This Book Is ForThis book is written for software engineers, data scientists, machine learning practitioners, researchers, and technically curious readers who want to move beyond surface familiarity with LLMs.You do not need to be an expert in machine learning to begin, but you should be comfortable with programming and willing to engage with ideas thoughtfully. Readers looking for quick tutorials or platform-specific recipes may want supplementary resources; readers seeking durable understanding will find this book invaluable.What This Book Is NotThis book does not promise instant mastery, viral tricks, or platform-specific shortcuts. It does not focus on prompt engineering in isolation, nor does it attempt to catalog every model variant or benchmark.Instead, it focuses on what lasts: the principles that explain why large language models work-and how to think clearly about the systems built around them.If you want to understand modern large language models deeply-not just use them-this book provi Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condición: new. Paperback. Data systems outlive applications, frameworks, and infrastructure.They encode decisions that shape what a system can become-and what it can never safely change.Designing Modern Data Systems is a deep, decision-driven guide to building data systems that are reliable, scalable, and adaptable over time. Rather than focusing on tools or trends, this book teaches how to reason about architecture itself: how guarantees are chosen, where authority lives, how failures manifest, and how systems evolve under real-world pressure.Written for experienced engineers and architects, the book treats data systems as long-lived sociotechnical systems-not just databases or pipelines. It focuses on clarity of responsibility, explicit trade-offs, and preserving meaning as data moves, changes, and ages.This book takes a structured journey through modern data system design: How to define data systems as distinct from applications and infrastructureHow non-functional requirements like reliability, availability, latency, and cost shape architecture long before technology choicesHow to design data models, storage engines, and indexing strategies that survive product evolutionHow to reason about replication, partitioning, coordination, and distributed transactions without accidental complexityHow batch and stream processing fit into a unified view of data over timeHow logs, history, and derived data enable recovery, reprocessing, and safe changeHow to operate systems in production with observability, backpressure, and failure isolationHow to design data systems that support machine learning and large language model platforms, including feature pipelines and embeddingsHow to migrate, evolve, and decommission systems without outages or loss of trustThroughout the book, ideas are grounded in a single evolving reference system, allowing readers to see how architectural decisions accumulate and interact as requirements change.What Makes This Book DifferentDecision-focused, not tool-drivenThe book avoids product comparisons and instead teaches how to evaluate any technology within clear architectural constraints.Explicit trade-offs, not recipesEvery design choice is examined in terms of what it enables, what it forbids, and what it costs.Modern, without being trendyAI and LLM systems are addressed where they introduce real architectural pressure-without hype or speculation.Written for longevityThe principles in this book are designed to remain relevant as tools, platforms, and organizational structures change.This book is written for: Software engineers designing backend and platform systemsData engineers responsible for storage, processing, and pipelinesStaff, principal, and senior engineers shaping architectural directionArchitects and technical leaders responsible for long-term system evolutionPractitioners preparing for system design interviews who want judgment, not templatesThis is not: A beginner's introduction to databasesA step-by-step tutorial for specific toolsA catalog of technologies or patternsInstead, it is a book about how to think clearly about data systems, and how to design them so they remain understandable, trustworthy, and changeable over time.If you are responsible for making architectural decisions-and living with their consequences-Designing Modern Data Systems is written for you. This item is printed on dema Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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