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Generative AI with Python: Build Image Generators, Chatbots, and Smart Apps
English | 18 Dec. 2025 | ASIN: B0G8KSFTG7 | 342 pages | Epub | 339.25 KB
Generative AI with Python is a practical, beginner-friendly guide for anyone who wants to move beyond AI theory and start building real, useful applications with confidence. This book takes you step by step into the world of generative artificial intelligence using Python, one of the most powerful and accessible programming languages today. You will learn how modern AI systems work, not just at a surface level, but in a way that helps you apply them to real problems. From generating images and text to building intelligent chatbots and smart assistants, the focus is always on hands-on learning and real-world use cases. You begin with clear foundations. The book explains core AI concepts, language models, and image generation in simple terms, making it suitable even if you are new to AI. As you progress, you move into practical development, using modern libraries and tools to generate images, customize styles, control outputs, and troubleshoot common issues. Every major concept is supported with working Python examples that you can run, modify, and build upon. A major strength of this book is how it connects individual skills into complete applications. You will learn how to create chatbots with memory, build document Q&A systems, design image generators with user interfaces, and combine vision and language models into smart multimodal apps. By the time you reach the later chapters, you will be developing end-to-end AI solutions that feel professional and production-ready. Beyond building apps, the book also prepares you for real opportunities. You will discover how to deploy and scale AI projects, manage costs, secure your applications, and turn your skills into freelance services, products, or business ideas. The final chapters guide you through portfolio building and staying relevant in the fast-changing AI industry. Written in a clear, conversational style, Generative AI with Python balances depth with simplicity. It avoids unnecessary jargon, focuses on what truly matters, and emphasizes learning by doing. Whether you are a student, developer, entrepreneur, or tech enthusiast, this book gives you the practical knowledge and confidence to build image generators, chatbots, and smart AI-powered applications that work in the real world. By the end of this book, you will not just understand generative AI. You will know how to use it, deploy it, and turn it into something valuable. Author: Luis Alexandra

Generative AI with LangChain for Beginners: Build Smart AI Agents, RAG Systems, and Real-World LLM Applications with Practical Python Tutorials
English | 17 Dec. 2025 | ASIN: B0G7RTRVYG | 378 pages | Epub | 2.64 MB
Generative AI with LangChain for Beginners: Build Smart AI Agents, RAG Systems, and Real-World LLM Applications with Practical Python Tutorials Overview This book is a practical, beginner-friendly guide to building real-world generative AI applications using LangChain and Python. It introduces you to the core ideas behind large language models and shows you how to move beyond simple prompts to create intelligent systems that reason, retrieve information, use tools, and operate reliably in production. Rather than focusing on theory alone, the book emphasizes hands-on learning and clear explanations that help you understand how modern AI applications actually work. You will learn how to design AI agents, build Retrieval-Augmented Generation systems, manage memory and tools, evaluate outputs, optimize performance, and deploy scalable applications. Each concept builds naturally on the previous one, making the learning journey smooth and confidence-building, even if you are new to LangChain or generative AI. The book starts by explaining how generative AI and LangChain fit together, then gradually guides you into building increasingly capable applications. You explore prompt design, chains, tools, and memory before moving into advanced topics like agent workflows, graph-based control, observability, evaluation, and cost optimization. Later chapters focus on deployment, security, monitoring, and real-world project walkthroughs that mirror production use cases. By the end, you are able to design, build, evaluate, and deploy complete AI systems with confidence using Python. Key Features of This Book This book focuses on practical, step-by-step learning using real examples that reflect how AI is used in professional environments. It covers AI agents, RAG pipelines, vector databases, observability with LangSmith, performance tuning, and deployment strategies. The content follows a clear progression from beginner concepts to production-ready applications, with an emphasis on clean design, reliability, and scalability. All tutorials are written in Python and aligned with modern LangChain workflows. Target Audience This book is ideal for beginners who want to enter generative AI development, Python developers looking to build LLM-powered applications, data engineers and software engineers exploring AI systems, and technical professionals who want a clear and practical introduction to LangChain, AI agents, and RAG systems. No advanced AI background is required, only basic Python knowledge and curiosity. If you want to stop experimenting with isolated prompts and start building real, reliable AI applications, this book is your guide. Dive in and learn how to design, build, and deploy intelligent generative AI systems with LangChain and Python one practical step at a time.

Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph
English | 23 May 2025 | ASIN: B0DYK6PKWM | 713 pages | EPUB (True) | 6.36 MB
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production-ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book Description This second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs-complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments. What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it's especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book. Table of Contents The Rise of Generative AI: From Language Models to Agents First Steps with LangChain Building Workflows with LangGraph Building Intelligent RAG Systems with LangChain Building Intelligent Agents Advanced Applications and Multi-Agent Systems Software Development and Data Analysis Agents Evaluation and Testing Observability and Production Deployment The Future of LLM Applications

Generative AI in Chemical Engineering
English | 23 Jan. 2026 | ASIN: B0GJFNFPWT | 278 pages | PDF | 3.77 MB
Chemical engineering has always thrived at the intersection of theory, experimentation, and computation. From hand calculations to process simulators and first-principles models, each technological shift has expanded what engineers can design, optimize, and understand. Today, generative artificial intelligence marks a new inflection point. Unlike traditional tools that analyze predefined scenarios, generative models can propose novel molecules, processes, control strategies, and data-driven hypotheses, often at a speed and scale previously unimaginable. This book explores how these capabilities reshape the chemical engineer's toolkit, not as a replacement for fundamental principles, but as a powerful complement that augments human insight with machine creativity. Generative AI in Chemical Engineering is written for students, researchers, and practitioners seeking to navigate this emerging landscape with both ambition and rigor. It bridges core chemical engineering concepts, thermodynamics, transport, kinetics, separations, and systems engineering with modern generative methods such as large language models, diffusion models, and reinforcement learning. Through practical examples, case studies, and critical discussions, the book aims to demystify the technology, highlight its limitations, and articulate best practices for responsible use. Ultimately, it invites readers to reimagine how chemical engineering problems are formulated and solved in an era where intelligent machines can generate possibilities as readily as engineers evaluate them.

Generative AI and the Rise of Artificial General Intelligence: The Simplified yet Powerful Guide to Grasping the Fundamentals, Real-World Applications, and Ethical Challenges
English | 22 Dec. 2025 | ASIN: B0GBYPKV6G | 154 pages | Epub | 2.41 MB
Generative AI and the Rise of Artificial General Intelligence Master Generative AI and Artificial General Intelligence in just weeks-even if you're brand new to tech, pressed for time, or think AI is "only for coders." Are you overwhelmed by the rapid pace of AI innovation, unsure how to start, or frustrated by dense, jargon-filled textbooks? Curious how generative AI can boost your studies, accelerate your career, or unleash your creativity-but worried you'll get lost in technical details? If you answered "yes," you're not alone. Generative AI and the Rise of Artificial General Intelligence was written for learners, innovators, and professionals of all backgrounds. With a clear, supportive, and step-by-step approach, this book breaks down barriers and provides you with the tools, context, and confidence to thrive in the new era of AI. Here's just a glimpse of what you'll discover inside: The 7 core foundations of AI -from machine learning basics to the principles behind generative models, explained with relatable analogies and clear diagrams. Prompt engineering decoded: Craft effective prompts with practical examples and real-world applications for GPT, DALL·E, and more. Hands-on projects and exercises throughout: Apply concepts immediately with interactive activities and learning checkpoints. Real-world case studies in healthcare, finance, education, creative arts, and more. A practical roadmap for upskilling: Discover which AI tools, platforms, and languages (including no-code and low-code options) can give you a career edge. Current and future trends: Stay ahead with concise insights on multimodal models, AGI, and next-generation AI breakthroughs. The most common ethical dilemmas in AI -and a clear framework to navigate them responsibly. Troubleshooting made easy: Learn proven methods for solving compatibility, integration, and implementation challenges-no matter your experience level. A guided walkthrough for building your first generative AI project-with annotated examples and non-coding alternatives. Visual learning support: Clear diagrams, infographics, and real-world illustrations to make complex ideas stick. The truth about AI hype: Distinguish between real innovation and fleeting trends with critical thinking prompts. Keep your skills relevant: Follow a personalized, adaptable learning path with curated resources for ongoing mastery and growth. Insights from leading AI practitioners: Actionable lessons and real-world perspectives to inspire your journey. Join the AI revolution: Discover thriving communities, global learning platforms, and collaboration opportunities to grow your skills and make an impact. A curated digital toolkit: Access supplementary materials, exercises, and recommended learning resources to deepen your understanding. ...and much more! Worried you don't have enough time, tech skills, or background to succeed with AI? This guide was built for real life. Each chapter is designed for busy schedules, with digestible sections, hands-on exercises, and clear takeaways that make every minute count. You'll never get lost in technical jargon or outdated theory-only clear, up-to-date insights and practical steps that meet you where you are. Whether you're just starting or looking to deepen your expertise, you'll find a welcoming, empowering path forward. Don't let the future of AI pass you by-scroll up and click "Add to Cart" to unlock the knowledge, tools, and inspiration you need to thrive in the age of generative intelligence!

Generative AI & Large Language Models: Concepts, Architectures, Applications, and Deployment
English | January 2, 2026 | ASIN: B0GDRY8KTX | 82 pages | Epub | 1.49 MB
Generative Artificial Intelligence is transforming how humans create, learn, decide, and innovate. From intelligent chatbots and virtual assistants to breakthroughs in healthcare, education, and law, Large Language Models (LLMs) are redefining the boundaries of machine intelligence. This comprehensive book provides a structured, end-to-end exploration of Generative AI and LLMs , starting from foundational concepts and progressing to advanced architectures, real-world applications, deployment strategies, and future directions. Designed to align with UG and PG syllabi , the book bridges the gap between theory, implementation, and industry practice . Key topics include: Foundations of Artificial Intelligence and Generative Models Transformer architecture and attention mechanisms Large Language Models such as GPT, BERT, and LLaMA Training, fine-tuning, and prompt engineering Retrieval-Augmented Generation (RAG) and multimodal AI Diffusion models and text-to-image systems Bias, hallucination, explainability, and responsible AI Chatbots, virtual assistants, and domain-specific applications LLM deployment, MLOps, scaling, monitoring, and optimization Future trends and ethical governance of Generative AI With clear explanations, case studies, industry insights, and ethical perspectives , this book serves as a textbook, reference guide, and practical handbook for students, educators, researchers, and professionals aiming to understand and build next-generation AI systems.

Yuma Nakamura, "Generation of Highly Controllable Ytterbium Atom Array: A Hybrid System of Nuclear Spin and Optical Clock Qubits Towards Quantum Error Correction"
English | ISBN: 9819528356 | | pages | PDF | 9 MB
This book offers a comprehensive exploration of a novel hybrid quantum computing platform based on dual-isotope ytterbium atom arrays. It presents pioneering work toward fault-tolerant quantum computation using nuclear spin and optical clock qubits in a neutral-atom system. The central challenge addressed in this book is the implementation of quantum error correction (QEC) in neutral-atom architectures, particularly the need for mid-circuit measurements-reading out ancilla qubits without disturbing data qubits. To overcome this, the author develops a hybrid array composed of fermionic 171Yb (serving as nuclear spin data qubits) and bosonic 174Yb (serving as optical clock ancilla qubits). This innovative combination enables non-destructive state readout and low crosstalk between qubits, laying critical groundwork for QEC protocols. This book demonstrates efficient state initialization and coherent control of both types of qubits in large-scale tweezer arrays. Through precise manipulation and rearrangement techniques, a defect-free, dual-isotope array is constructed with high single-site fidelity and minimal dual occupancy. Crosstalk analysis reveals that 174Yb imaging light does not degrade the coherence of adjacent 171Yb nuclear spin qubits-an essential result for practical QEC. In parallel, the work reports the development of a high-power, narrow-linewidth ultraviolet laser at 325 nm to achieve coherent Rydberg excitation of ytterbium atoms. The laser system, which includes a Raman fiber amplifier and high-finesse cavity for noise suppression, enables MHz-order Rabi oscillations between metastable and Rydberg states. This achievement provides a path toward implementing high-fidelity two-qubit gates, a cornerstone of universal quantum computation. This book combines theoretical insights, advanced laser engineering, and precision atomic control to establish a scalable, neutral-atom-based quantum computing architecture. Its interdisciplinary approach makes it a valuable resource not only for physicists working in quantum optics and atomic physics but also for engineers and computer scientists interested in next-generation quantum technologies.

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Dl4All - Download free For All: software and educational resource.

Generative AI with Python Build Image Generators, Chatbots, and Smart Apps

Generative AI with LangChain for Beginners Build Smart AI Agents, RAG Systems, and Real-World LLM Applications with Practical

Generative AI with LangChain Build production-ready LLM applications and advanced agents using Python, LangChain

Generative AI on Google Cloud with LangChain

Generative AI in Chemical Engineering

Generative AI for Cloud Solutions Architect modern AI LLMs in secure, scalable, and ethical cloud environments

Generative AI and the Rise of Artificial General Intelligence The Simplified yet Powerful Guide to Grasping the Fundamentals

Generative AI 2.0 Beyond Text and Image

Generative AI & Large Language Models Concepts, Architectures, Applications, and Deployment

Generation of Highly Controllable Ytterbium Atom Array A Hybrid System of Nuclear Spin and Optical Clock Qubits