Los modelos de lenguaje de gran tamaño (LLM) y los modelos de difusión, como ChatGPT y DALL-E, representan una revolución sin precedentes en el ámbito de la inteligencia artificial. Entrenados con texto e imagenes publicas disponibles en Internet, estos modelos son capaces de abordar una amplia variedad de tareas, ofreciendo soluciones innovadoras y automatizadas a problemas previamente inabordables.A pesar de su accesibilidad, muchos desarrolladores enfrentan desafios significativos al intentar obtener resultados fiables y utilizables en sistemas automatizados. Este libro ofrece una guia exhaustiva para adquirir una solida base en IA generativa y aprender a aplicar estos modelos en la practica. Los autores, James Phoenix y Mike Taylor, presentan los principios del prompt engineering, esenciales para trabajar de manera efectiva con IA en entornos de produccion.En este libro descubrira:- Los cinco principios de prompting que son transferibles entre diferentes modelos y que mantendran su efectividad en el futuro.- Aplicaciones practicas de la IA generativa en ejemplos del mundo real, utilizando bibliotecas y frameworks como LangChain.- Como evaluar y comparar modelos: analizara y comparara modelos de OpenAI, como GPT-4 y DALL-E 2, con alternativas de codigo abierto, identificando sus fortalezas y debilidades.- Implementacion en diversos ambitos: aprendera como aplicar estos principios en procesamiento del lenguaje natural (PNL), generacion de texto e imagenes, y generacion de codigo.Este libro es una herramienta indispensable para cualquier desarrollador que quiera llevar sus proyectos al siguiente nivel y aprovechar el poder de la IA generativa para transformar sus resultados y diferenciarse en el mercado. No lo deje escapar.

Build, Scale and Optimize Cutting-Edge NLP with Llama for Next Gen AI. Key Features Explore Llamas evolution and innovations for next-gen NLP. Implement real-world NLP tasks with step-by-step examples. Fine-tune, optimize, and deploy Llama at enterprise scale. Book Description Llama models have rapidly emerged as a cornerstone in natural language processing, redefining how AI systems understand and generate human language. From their efficient architecture to the cutting-edge advancements in Llama 4, these models enable enterprises, researchers, and developers to build powerful, scalable, and responsible NLP solutions. This book, Ultimate Llama for Natural Language Processing (NLP), takes you on a structured journey through the evolution and applications of Llama. It begins with the foundations of the Llama series and its architecture, before progressing to core NLP tasks such as classification, summarization, sentiment analysis, and conversational AI. Subsequent chapters cover fine-tuning, transfer learning, optimization, and deployment at enterprise scale, with practical insights into real-world industry use cases. The book also addresses troubleshooting, ethical AI, and the future of multimodal and sparse Mixture-of-Experts models. Thus, by the end, readers will be well-equipped to train, adapt, and deploy Llama models across domains such as healthcare, finance, and customer engagement. What you will learn Understand Llamas evolution, architecture, and unique innovations in NLP. Implement core NLP tasks like classification, NER, and summarization. Fine-tune Llama for custom domains using advanced transfer learning. Optimize inference speed, and deploy Llama models at enterprise scale. Troubleshoot, monitor, and continuously improve Llama model performance. Apply Llama 4 to real-world industry use cases and multimodal AI.

Build Real-World AI with Transformers Powered by PyTorch 2.0. Key Features Complete hands-on projects spanning NLP, vision, and speech AI. Interactive Jupyter Notebooks with real-world industry scenarios. Build a professional AI portfolio ready for career advancement. Book Description Transformer models have revolutionized AI across natural language processing, computer vision, and speech recognition. "Ultimate Transformer Models Using PyTorch 2.0" bridges theory and practice, guiding you from fundamentals to advanced implementations with hands-on projects that build a professional AI portfolio. This comprehensive journey spans 11 chapters, beginning with transformer foundations and PyTorch 2.0 setup. With this book, you will master self-attention mechanisms, tackle NLP tasks such as text classification and translation, and then expand into computer vision and speech processing. Advanced topics include BERT and GPT models, the Hugging Face ecosystem, training strategies, and deployment techniques. Each chapter features practical exercises that reinforce learning through real-world applications. By the end of this book, you will be able to confidently design, implement, and optimize transformer models for diverse challenges. So, whether revolutionizing language understanding, advancing computer vision, or innovating speech recognition, you will possess both theoretical knowledge and practical expertise to deploy solutions effectively across industries like healthcare, finance, and social media, positioning yourself at the AI revolutions forefront. What you will learn Build custom transformer architectures from scratch, using PyTorch 2.0. Fine-tune BERT, GPT, and T5 models for specific applications. Deploy production-ready AI models across NLP, vision, and speech domains. Master Hugging Face ecosystem for rapid model development and deployment. Optimize transformer performance, using advanced training techniques and hyperparameters. Create a professional portfolio showcasing real-world transformer implementations.

Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. Key Features Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP. Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL. Advanced coverage of evolutionary and metaheuristic algorithms. Integration of Genetic Algorithms with generative and agent-based AI systems. Book Description Genetic Algorithms (GAs) are nature-inspired optimization tools that help AI systems adapt, improve, and solve complex problems efficiently. Ultimate Genetic Algorithms with Python explains elaborately the fundamentals of GAs to practical, Python-based implementation, using PyGAD and DEAP. The book starts with a solid foundation, explaining how evolutionary principles can be applied to optimization tasks, search problems, and model improvement. You will also explore GA applications across multiple AI domains: optimizing machine learning workflows, evolving neural network architectures in deep learning, enhancing feature selection in NLP, improving performance in computer vision, and guiding exploration strategies in reinforcement learning. Each application chapter includes step-by-step coding examples, performance comparisons, and tuning techniques. The later sections focus on advanced metaheuristics, swarm intelligence, and integrating GAs with generative and agent-based AI systems. You will also learn how to design self-evolving, multi-agent frameworks, leverage swarm-based methods, and connect GAs to next-gen AI architectures such as Model Context Protocols (MCP). What you will learn Master the fundamentals and components of Genetic Algorithms. Implement GAs in Python, using PyGAD, DEAP, and PyTorch. Apply GAs for optimization, feature selection, and neural architecture search. Enhance AI workflows in ML, DL, NLP, CV, and RL with GAs. Explore metaheuristic and swarm-based algorithms for complex problem-solving. Integrate GAs into generative, multi-agent, and self-evolving AI systems.

Turn Financial Data into Decisions with the Power of FinGPT. Key Features Hands-on setup of FINGPT in real-world finance projects. End-to-end guide for automating financial reporting tasks. Case studies on market trends and sentiment prediction. Techniques to scale, fine-tune, and optimize FINGPT models. Book Description FINGPT is redefining how financial institutions analyze data, forecast trends, and make strategic decisions. As the financial sector embraces generative AI, understanding and applying FINGPT becomes essential for professionals seeking to stay competitive and innovative. Ultimate FINGPT for Financial Analysis takes you on a complete journeyfrom setting up your development environment and preparing financial datasets to building, fine-tuning, and deploying FINGPT models. The book covers all the vital concepts such as data cleaning, model training, prompt engineering, and real-world deployment. You will learn to automate financial reporting, generate accurate forecasts, perform sentiment analysis on news and reports, and simulate risk scenarios. Dedicated chapters on case studies and performance optimization provide deep insights into practical applications, while ethical considerations and scaling strategies ensure readiness for enterprise use. Hence, whether you are a finance expert aiming to integrate AI or a data scientist expanding into fintech, this book provides the tools, frameworks, and confidence to apply FINGPT in your work. So, do not get left behindstart transforming your financial analysis with AI today. What you will learn Apply FINGPT for financial forecasting and workflow automation. Build sentiment-aware models for trend and event prediction. Combine structured and unstructured data for deep insights. Generate reports and analytics, using AI-powered pipelines. Simulate risk scenarios, and plan proactive mitigations. Monitor FINGPT performance, using finance-specific KPIs.

Code smarter, Test faster, and Build better with GitHub Copilot! Key Features Master prompt engineering and multi-modal Copilot interactions. Use GitHub Copilot for real coding, testing, and DevOps tasks. Speed up development with AI-powered code and test generation. Book Description AI-assisted coding is transforming how software is builtfaster, smarter, and with fewer errors. GitHub Copilot leads this revolution by turning natural language into functional code, enabling developers to focus on solving problems rather than writing boilerplate. The Ultimate AI-Assisted Development with GitHub Copilot takes you step-by-step through mastering Copilot, starting with initial setup and basic use across multiple languages like Java, Python, TypeScript, Go, and C++. Youll explore prompt engineering techniques to craft effective instructions, leverage multi-modal inputs to interact beyond text, and unlock advanced features like Vibe Coding and Agent Mode to create context-aware, intelligent workflows. The book also covers integrating Copilot into testing and debugging processes, automating repetitive tasks, and embedding AI-powered coding into CI/CD pipelines to streamline DevOps practices. Whether youre building APIs, automating tests, refactoring code, or optimizing release workflows, this book teaches you how to collaborate with AInot just use it. Dont get left behindunlock the full potential of GitHub Copilot and future-proof your skills today. What you will learn Use GitHub Copilot effectively in Python, Java, Go, and C++. Write smart prompts to guide Copilot across coding scenarios. Build and debug applications using AI-generated code snippets. Enhance test automation and integrate Copilot into CI/CD flows. Leverage Agent Mode and Vibe Coding for intelligent automation. Adapt Copilot for education, framework design, and DevOps tasks.

Unleash Agility and Scale with Micro Frontend Architecture. Key Features Gain a solid understanding of micro frontend principles, patterns, and benefits. Explore integration strategies with real-world examples and practical guidance. Discover best practices for adoption, testing, deployment, and long-term scalability. Book Description As modern web applications grow in complexity, micro frontend architecture offers a scalable, modular approach that helps teams work independently, accelerate delivery, and maintain long-term flexibility. Micro Frontends Architecture for Scalable Applications provides a comprehensive, hands-on guide to implementing micro frontends effectively in real-world projects. The book starts by defining frontend and backend development, exploring what is considered a valuable architecture, and introducing various frontend architecture patterns. Next, it dives into micro frontends and concepts like Domain-Driven Design, Decentralized Governance, and Independent Deployments. It explains how these principles enable highly observable (easy-to-monitor) micro frontends and covers in-depth discussions on integration strategies, their advantages and disadvantages. Communication and routing are thoroughly explored, including Container Applications, State Management, and Cross-Application Communication. The book also addresses UI/UX considerations and how micro frontends interact with varying backend patterns. The final part focuses on best practices for testing (unit, integration, and end-to-end), deployment strategies, and essential security measures. It concludes with a real-world case study and insights into the future of micro frontendshighlighting emerging trends, evolving architectures, and the growing impact of artificial intelligence. What you will learn Design scalable frontend architectures using modular principles. Choose and apply the right integration strategy per use case. Build CI/CD pipelines optimized for micro frontend deployments. Handle routing, shared state, and communication across applications. Ensure UI/UX consistency with design systems in distributed teams. Explore how AI and emerging trends impact frontend architectures.

Transform Text into Intelligent Conversations with LangChain and GPT. Key Features Build AI Chatbots with LangChain, Python and GPT models through hands-on guidance. Master Advanced Techniques like RAG, document embedding, and LLM fine-tuning. Deploy and Scale conversational AI systems for real-world applications. Book Description Conversational AI Apps are revolutionizing the way we interact with technology, enabling businesses and developers to create smarter, more intuitive applications that engage users in natural, meaningful ways. Building Conversational Generative AI Apps with LangChain and GPT is your ultimate guide to mastering AI-driven conversational systems. Starting with core concepts of generative AI and LLMs, youll learn to build intelligent chatbots and virtual assistants, while exploring techniques like fine-tuning LLMs, retrieval-augmented generation (RAG), and document embedding. As you progress, youll dive deeper into advanced topics such as vector databases and multimodal capabilities, enabling you to create highly accurate, context-aware AI agents. The books step-by-step tutorials ensure that you develop practical skills in deploying and optimizing scalable conversational AI solutions. By the end, youll be equipped to build AI apps that enhance customer engagement, automate workflows, and scale seamlessly. Unlock the potential of Building Conversational Generative AI Apps with LangChain and GPT and create next-gen AI applications today! What you will learn Build and deploy AI-driven chatbots using LangChain and GPT models. Implement advanced techniques like retrieval-augmented generation (RAG) for smarter responses. Fine-tune LLMs for domain-specific conversational AI applications. Leverage vector databases for efficient knowledge retrieval and enhanced chatbot performance. Explore multimodal capabilities and document embedding for better context-aware responses. Optimize and scale conversational AI systems for large-scale deployments.

La colección Gregorio Peces-Barba pretende servir para reflexionar de manera actual y sucinta sobre conceptos jurídicos fundamentes para el Derecho.Este número aborda los retos normativos de la Inteligencia Artificial. Desde una perspectiva interdisciplinar, con enfoque juridico, se trata este tema de gran actualidad y que suscita gran interes.Entre otras cuestiones se reflexiona sobre las dimensiones etico-juridicas de la Inteligencia Artificial, los aspectos relacionados con los modos de regulacion los desafios que para los derechos humanos conlleva la IA.La coleccion Gregorio Peces-Barba esta especializada en publicar debates y monografias breves. Es una coleccion impulsada desde el Departamento de Derecho Internacional, Eclesiastico y Filosofia del Derecho de la Universidad Carlos III de Madrid.

El campo de la inteligencia artificial (IA) y el Derecho está en la cúspide de una revolución que comenzó con programas de análisis de texto como Watson y Debater y las arquitecturas de gestión de la informacion de codigo abierto en las que se basan. Hoy en dia, estan empezando a aparecer nuevas aplicaciones juridicas, y este libro diseñado para explicar los procesos computacionales a los no programadores describe como cambiaran la practica del Derecho al conectar modelos computacionales de razonamiento juridico directamente con el texto juridico, generar argumentos a favor y en contra de determinados resultados, predecir resultados y explicar estas predicciones con razones que los profesionales del Derecho podran evaluar por si mismos. Estas aplicaciones juridicas apoyaran la recuperacion de informacion juridica conceptual y permitiran la computacion cognitiva, posibilitando una colaboracion entre humanos y ordenadores en la que cada uno realice el tipo de actividades inteligentes que mejor sepa hacer. Cualquiera que este interesado en como la IA esta cambiando el ejercicio de la abogacia deberia leer esta esclarecedora obra.

Build Future-ready Enterprise AI with IBM Granite and Watsonx AI. Key Features Step-by-step instructions to install and configure Granite 3.0 using GitHub and VSCode on both Windows and Red Hat Linux systems. Build advanced prompt strategies using Python and Ansible to enhance LLM response accuracy and adaptability in enterprise scenarios. Prepare for the future of enterprise AI with forward-looking analysis of IBMs Granite platform evolution. Book Description IBM Granite 3.0 delivers enterprise-ready, open-source LLMs powered by Watsonx AImaking it essential for developers, data scientists, and IT professionals driving AI transformation. Ultimate IBM Granite for Enterprise Applications offers a structured, hands-on journey from foundational knowledge to real-world enterprise deployment. You will start by learning the principles of LLMs, and then unpack the architecture of IBM Granite 3.0. guided tutorials to walk you through the installation process using GitHub and VSCode, empowering you to replicate the setup on your systems. As you navigate through the book, you will delve deeper into advanced prompt engineering techniques to improve accuracy and performance, fine-tune Granite models for specialized business domains, and streamline DevOps automation using Red Hat Ansible Lightspeed. Throughout the book, code examples and Ansible playbooks ensure concepts that are not just explained, but applied. In the final chapters, you will focus on scaling, deploying, and maintaining Granite LLMs in production environmentscomplete with strategies for performance optimization, version upgrades, and enterprise integration. The book concludes with a forward-looking view into IBMs AI roadmap, helping you to align your strategy with what is next. Hence, whether you are building intelligent apps, modernizing DevOps, or planning long-term AI strategies, this book equips you to harness Granite 3.0s full power. What you will learn Set up IBM Granite 3.0, using GitHub and VSCode environments. Understand how IBMs LLM architecture powers enterprise-scale solutions. Automate system configuration, using Red Hat Ansible Lightspeed. Fine-tune Granite models to suit your domain-specific needs. Optimize AI deployments for enterprise performance and reliability. Anticipate Future AI Trends with IBMs Granite Roadmap.

Your Complete Roadmap to AWS AI Practitioner SuccessSimplified, Practical, and Designed to Help You Pass with Confidence. Key Features Gain in-depth knowledge of AWS AI services, Generative AI, and ethical considerations for business and technical use cases. Master essential AWS AI/ML tools to stay ahead in the evolving landscape of cloud-based artificial intelligence solutions. Prepare confidently with real-world examples, clear explanations, and targeted exam questions for the AWS AI Practitioner certification. Book Description In todays AI-powered world, earning the AWS Certified AI Practitioner (AIF-C01) certification is a powerful way to validate your skills, boost your credibility, and stand out in the competitive cloud job market. Ultimate AWS Certified AI Practitioner (AIF-C01) Exam Guide is a comprehensive, beginner-friendly roadmap for professionals, students, and decision-makers looking to master AI and Machine Learning on AWSand crack the AIF-C01 exam with confidence. Covering everything from AI and ML fundamentals to core AWS services like SageMaker, Bedrock, and Rekognition, this guide also explores Generative AI, vision and language-based AI use cases, and practical tools for personalization, security, and governance. Youll gain clarity on responsible AI principles, learn to identify and mitigate bias, and confidently navigate AWS best practices in ethics and compliance. Each chapter offers real-world examples, exam strategies, and practice questions designed to reinforce key concepts and simulate the exam environment. Whether youre technical or non-technical, the content is simplified for easy understandingwithout sacrificing depth or relevance. If youre serious about working in AI or cloud, this certification isnt just a bonusits becoming a must-have. Dont miss your chance to stay ahead of the curvemaster AWS AI and future-proof your career now. What you will learn Understand foundational concepts of AI, Machine Learning, and Generative AI for modern cloud applications. Gain hands-on experience with AWS AI/ML services like SageMaker, Bedrock and Rekognition to build intelligent solutions. Learn to build, train, fine-tune, and deploy machine learning models using Amazon SageMaker. Apply responsible AI practices by identifying and mitigating ethical risks, biases, and fairness issues in AI solutions. Secure your AI workloads through AWS best practices in governance, compliance, and data protection. Access targeted exam tips, mock questions, and real-world examples to confidently clear the AWS AI Practitioner certification.

Empowering Enterprises with Scalable, Intelligent AI Agents. Key Features Hands-on practical guidance with step-by-step tutorials and real-world examples. Build and deploy enterprise-grade LLM agents using the AutoGen framework. Optimize, scale, secure, and maintain AI agents in real-world business settings. Book Description In an era where artificial intelligence is transforming enterprises, Large Language Models (LLMs) are unlocking new frontiers in automation, augmentation, and intelligent decision-making. Ultimate Agentic AI with AutoGen for Enterprise Automation bridges the gap between foundational AI concepts and hands-on implementation, empowering professionals to build scalable and intelligent enterprise agents. The book begins with the core principles of LLM agents and gradually moves into advanced topics such as agent architecture, tool integration, memory systems, and context awareness. Readers will learn how to design task-specific agents, apply ethical and security guardrails, and operationalize them using the powerful AutoGen framework. Each chapter includes practical examplesfrom customer support to internal process automationensuring concepts are actionable in real-world settings. By the end of this book, you will have a comprehensive understanding of how to design, develop, deploy, and maintain LLM-powered agents tailored for enterprise needs. Whether youre a developer, data scientist, or enterprise architect, this guide offers a structured path to transform intelligent agent concepts into production-ready solutions. What you will learn Design and implement intelligent LLM agents using the AutoGen framework. Integrate external tools and APIs to enhance agent functionality. Fine-tune agent behavior for enterprise-specific use cases and goals. Deploy secure, scalable AI agents in real-world production environments. Monitor, evaluate, and maintain agents with robust operational strategies. Automate complex business workflows using enterprise-grade AI solutions.

Power your AI Journey and Build the Future with Snowflake Cortex. Key Features Build enterprise-ready GenAI apps using Snowflake Cortex tools and APIs. Implement RAG, AI Agents, and Document AI with real-world precision. Explore practical Cortex use cases across industries and domains. Book Description Snowflake Cortex is redefining how modern enterprises build, scale, and deploy Generative AInatively within the data cloud. Ultimate Snowflake Cortex AI for Generative AI Applications is a hands-on, end-to-end guide designed for data professionals, engineers, and technical leaders eager to unlock the full power of Snowflakes native AI engine. The book begins by grounding you in the fundamentals of AI/ML within the Snowflake ecosystem before diving deep into the architecture, capabilities, and use cases of Snowflake Cortex. As you progress, youll explore Cortexs built-in machine learning functions, dive into prompt engineering, Retrieval-Augmented Generation (RAG), and learn how to leverage LLM functions effectively. Youll gain hands-on experience in fine-tuning models, translating natural language queries into actionable insights, and automating document processing using Cortexs Document AI. Practical chapters on security, governance, and cost discipline ensure youre prepared for enterprise-scale AI deployment. With real-world case studies and cross-industry applications, this book equips you with both the strategic understanding and technical skills to implement Generative AI at scale. Cortex is the future of enterprise AIdont just adapt to it, lead it. What you will learn Build and deploy Generative AI apps using Snowflake Cortex. Understand and apply Cortexs built-in LLM functions effectively. Fine-tune LLMs for domain-specific, enterprise-grade applications. Use RAG and prompt engineering for accurate AI responses. Extract insights from structured and unstructured enterprise data. Automate document workflows using Cortexs Document AI features. Solve cross-industry problems with real-world Cortex implementations.

Harness AI-led Revolution to Deliver Smarter, Faster, and Better Healthcare. Key Features Comprehensive coverage of AI tools, use cases, and strategies in healthcare Real-world examples from telemedicine, diagnostics, and EHR systems Thoughtful exploration of AI ethics, adoption barriers, and future directions Book Description Artificial Intelligence is rapidly becoming indispensable in modern healthcarereshaping how we diagnose, treat, and deliver care. Mastering AI for Healthcare offers a clear, structured journey through this transformation, helping readers understand the critical role AI plays in improving clinical outcomes and operational efficiency. The book begins with foundational AI concepts tailored for healthcare professionals, establishing a solid base in machine learning, neural networks, and data science. It then guides readers through practical applications across the care continuumexploring how predictive analytics can forecast disease risks, how AI powers telemedicine and remote monitoring systems, and how it enhances clinical decision-making through smarter, data-driven EHR systems. Each chapter builds with real-world examples, case studies, and implementation frameworks, offering clarity on emerging tools like computer vision for diagnostics, personalized treatment algorithms, and intelligent automation in hospital workflows. Whether youre a healthcare professional, researcher, or policymaker, this book provides the insights you need to stay ahead in a tech-driven world. Dont get left behindembrace the tools shaping the future of medicine and lead the change with confidence. What you will learn Grasp essential AI concepts and their relevance to healthcare systems Apply predictive analytics to real-world patient and clinical scenarios Utilize AI in telehealth, remote monitoring, and digital health platforms Strengthen decision-making through AI-powered EHR and clinical support Interpret AI-driven diagnostic tools and imaging technologies Recognize ethical, legal, and operational challenges in AI integration

Build Real-time IoT Solutions Using Python, Edge, and Docker Tools. Key Features Explore real-world IoT use-cases using Python and edge computing. Hands-on projects on embedded devices like Raspberry Pi and Jetson Nano. Learn edge-cloud integration, Docker, and protocols such as MQTT and CoAP. Covers security, streaming data, and future edge computing trends. Book Description The Internet of Things (IoT) is reshaping the way the world collects, processes, and responds to datafrom smart homes and wearables to autonomous vehicles and industrial automation. As the demand for fast, secure, and intelligent data handling grows, edge computing becomes a key enabler, pushing computation closer to the source and reducing latency, cloud dependency, and security risks. Practical IoT Edge Computing with Python equips readers with a complete, hands-on roadmap to build robust, real-time IoT systems, using edge devices and Python. You will begin by understanding the core concepts of IoT, and the limitations of traditional cloud-based models. Thus, step by step, you will move through building an IoT data pipeline, integrating edge and cloud, and deploying on devices like Raspberry Pi and Jetson Nano, as well as working with protocols such as MQTT and CoAP. You will also gain practical experience with data preprocessing, edge intelligence, containerization (Docker/Kubernetes), and security measures like blockchain. Each chapter builds your confidence to design scalable, secure, and responsive IoT systems. Hence, whether you are a student, developer, or industry professional, this book offers the tools and knowledge to turn IoT concepts into fully functioning edge solutions. What you will learn Understand edge vs. cloud computing in IoT system architectures. Build IoT data pipelines including pre-processing and analysis steps. Deploy Python-based data processing on embedded edge platforms. Use networking protocols (MQTT, CoAP and AMQP) in edge devices. Containerize models with Docker/Kubernetes for edge deployment. Secure IoT systems with blockchain, and understand privacy challenges.

Este libro surge a raíz de la necesidad de crear un compendio de prácticas para iniciar a alumnos de formación profesional de grado medio, grado superior y bachillerato a la robótica y a la domótica mediante la plataforma de Arduino._x000D_La estructura del libro permite diferenciar tres partes o bloques: el primero dedicado a exponer unos pocos conceptos sobre Arduino y las disciplinas que lo rodean; el segundo dedicado a la explicacion del lenguaje de programacion de Arduino; y el tercer bloque se dedica al aprendizaje de Arduino a traves de una serie de practicas, donde se expone un componente electronico o sensor._x000D_Cada practica posee el codigo totalmente explicado, comentado y analizado._x000D_Este libro propone una serie de 28 practicas detalladas y comentadas a docentes que deseen incorporar Arduino en alguna de sus asignaturas, y de guia de referencia y aprendizaje para aquellos lectores que puedan considerarse noveles en Arduino._x000D_Por tanto, el lector va aprendiendo mediante practicas, aisladas en apariencia, como manejar la placa Arduino para despues crear sus propios proyectos.

Crea tu propio asistente inteligente en solo 15 minutos y descubre cómo la inteligencia artificial puede transformar tu trabajo diario. Este libro te guía, paso a paso, para que configures tus propios GPT personalizados, incluso sin experiencia previa en programacion o tecnologia. Descubriras como automatizar tareas repetitivas, redactar correos, generar contenidos, analizar documentos y mucho mas. Todo con unas simples indicaciones.Con explicaciones claras y ejemplos practicos, aprenderas a: Diseñar GPT adaptados a tus necesidades especificas. Configurar instrucciones precisas para tareas complejas Descubrir y sacar el maximo partido a los mejores GPT disponibles. Optimizar tus creaciones con estrategias efectivas.Con un enfoque etico y accesible, esta guia esta dirigida a profesionales, estudiantes y cualquier persona que desee mejorar su creatividad y productividad sin complicaciones tecnicas. Una puerta de entrada a un nuevo nivel de eficiencia y oportunidades.

La fascinante historia de OpenAI y ChatGTP, y del catastrófico destino al que nos pueden conducirCon un acceso privilegiado a las personas más cercanas a Sam Altman, líder de OpenAI, Karen Hao descubre como esta organizacion se ha convertido en el epicentro de una industria tecnologica con poder y recursos sin precedentes. Asi, narra su frenetica culminacion en el sector y expone las enormes tensiones eticas, sociales y medioambientales que esta nueva era esta generando.

LA TECNOLOGÍA VISTA CON LOS OJOS DE LAS PERSONAS QUE LA PRODUCEN. «Arroja luz sobre los rincones más oscuros de esta "revolución" y expone su enorme costo humano. Qué libro tan importante». Stephen F