Novedades del último mes en eBooks de Inteligencia artificial

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.

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.

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.