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Generative AI Engineering: Build apps with transformer and diffusion-based large and foundational models
by Konrad BanachewiczApply creativity and engineering to create apps with transformer and diffusion based large and foundational models Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn with practical examples, code snippets, and use cases from a variety of generative AI applicationsConquer the core concepts and techniques of generative AI engineeringGet to grips with productionizing operational pipelines with generative AIBook DescriptionGenerative AI Engineering is a hands-on guide to utilizing generative AI for creating advanced AI based applications. It’s designed for both beginners and experienced practitioners who want to learn how to develop generative AI models-based applications and use them to solve real-world problems. As you progress through the chapters, you’ll cover all the core concepts and techniques of generative AI engineering, including transformer and diffusion-based models. You'll also learn from practical examples and carefully chosen code snippets that will help you understand how to apply these concepts to real-world problems. You will learn the generative AI services and platforms offered by major cloud providers, GCP, AWS, and Microsoft Azure. With its comprehensive coverage and practical examples, this book will provide you with the skills and knowledge you need to build cutting-edge AI applications that can create new content, generate realistic images and videos, and solve complex problems in a wide range of industries.What you will learnGet to grips with the fundamentals of generative AI and its applicationsFamiliarize yourself with different types of generative models and when to use themTrain and Finetune generative models using PyTorchEvaluate the performance of your models and fine-tune them for optimal resultsFind best practices for deploying and scaling generative AI models in production environmentsWho this book is forWhether you are a software engineer, data scientist, or AI enthusiast, Generative AI Engineering is an essential resource that will help you master the art of building generative AI models. To get started, you’ll need to have basic knowledge of Python coding, including Jupyter Notebooks, setup cloud or local infrastructure using Docker containers for sample code, and deep learning and PyTorch.
Generative AI For Dummies
by Pam BakerGenerate a personal assistant with generative AI Generative AI tools capable of creating text, images, and even ideas seemingly out of thin air have exploded in popularity and sophistication. This valuable technology can assist in authoring short and long-form content, producing audio and video, serving as a research assistant, and tons of other professional and personal tasks. Generative AI For Dummies is your roadmap to using the world of artificial intelligence to enhance your personal and professional lives. You'll learn how to identify the best platforms for your needs and write the prompts that coax out the content you want. Written by the best-selling author of ChatGPT For Dummies, this book is the ideal place to start when you're ready to fully dive into the world of generative AI. Discover the best generative AI tools and learn how to use them for writing, designing, and beyond Write strong AI prompts so you can generate valuable output and save time Create AI-generated audio, video, and imagery Incorporate AI into your everyday tasks for enhanced productivity This book offers an easy-to-follow overview of the capabilities of generative AI and how to incorporate them into any job. It's perfect for anyone who wants to add AI know-how into their work.
Generative AI For Executives: A Strategic Roadmap for Your Organization
by Ahmed Bouzid Weiye Ma Paolo NarcisoIn the fast-evolving digital landscape, understanding the potential of generative AI is a strategic advantage. This book can serve as an easy to read introduction to the topic of the transformative power of AI in content creation, customer engagement, and operational efficiency. By deciphering complex AI concepts into practical insights, we empower decision-makers to envision innovative strategies, foster cross-industry collaborations, and navigate ethical considerations. The book will help executives and business decision makers to harness the immense potential of generative AI responsibly, ensuring data integrity and compliance while fostering a competitive edge. The book is focused on (1) Explaining in jargon-free language what Generative AI, and AI in general, (2) What problems they solve, and (3) What technologies make them possible.What You Will LearnHow generative AI models are built, how they generate new data or content, and the underlying algorithms powering these processesVarious practical applications of generative AI in business contextsThe challenges that could arise during the integration of generative AI into business processesWho This Book is ForThis book is meant to be bought and read by busy executives and business leaders
Generative AI Security: Theories and Practices (Future of Business and Finance)
by Sean Wright Ben Goertzel Yang Wang Ken Huang Yale Li Jyoti PonnapalliThis book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity.
Generative AI and Cyberbullying
by Ravindra DasEver since the COVID-19 pandemic occurred in 2020, the world has transformed itself greatly. For example, not only is the near-99% remote workforce now a reality, but businesses today are taking incident response and disaster recovery much more seriously these days as well. But another area that has blossomed in the last couple of years has been that of Generative AI. It is actually a subfield of artificial intelligence, which has been around since the 1950s.But Gen AI (as it is also called) has been fueled by the technology of ChatGPT, which has been developed and created by OpenAI. Given the GPT4 algorithms Gen AI is powered by, an end user can merely type in, or even speak into the platform a query, and an output that is specific to that query will be automatically generated. The answer (or "output") can be given as a text, video, image, or even an audio file.The scalability and diversity of Gen AI has allowed it to be used in a myriad of industries and applications. But although it has been primarily designed to serve the greater good, it can also be used for very nefarious purposes, such as online harassment and Cyberbullying.In this particular book, we actually take the good side of Gen AI and provide an overview as to how it can be used to help combat Cyberbullying. This book is broken down into the following topics: What Cyberbullying is all about How Gen AI can be used to combat Cyberbullying An overview into Gen AI Advanced topics into Gen AI Conclusions
Generative AI and Digital Forensics
by Ravindra DasIn today’s world, cybersecurity attacks and security breaches are becoming the norm. Unfortunately, we are not immune to it, and any individual and entity is at dire risk. The best and only thing that we can do is to mitigate the risks as much as much as possible so that they do not happen at all. But even when a security breach does indeed happen, the immediate reaction is to contain it so that it does not penetrate further into the information technology/network infrastructure. From there, mission-critical processes need to be restored, until the business can resume a normal state of operations, like it was before the security breach.But another key step here is to investigate how and why the security breach happened in the first place. The best way to do this is through what is known as “digital forensics”. This is where specially trained digital forensics investigators collect and comb through every piece of evidence to determine this. Eventually, the goal is then to use this evidence in a court of law so the cyberattacker can be made to answer for their crime and eventually be brought to justice.However, the area of digital forensics is a large one, and many topics around it can be covered. Also, generative AI is being used to not only help in the analysis of the evidence but also to help automate the digital forensics software packages that are available today. Therefore, in this book, the following is covered: Examples of security breaches and overview of digital forensics. How digital forensics can be used to investigate the loss or theft of data. An introduction to the SQL Server Database. A review of the SQL Injection Attack. How generative AI can be used in digital forensics.
Generative AI for Academics
by Mark CarriganThis is your indispensable guide to navigating the rise of generative AI as an academic. It thoughtfully explores rapidly evolving AI capabilities reshaping higher education, examining challenges and ethical dilemmas across the sector. It provides useful strategies for using generative AI in your scholarly work while upholding professional standards. This practical guidance addresses four core areas of academic work: Thinking: How to use generative AI to augment individual and collaborative scholarly thinking that can assist in developing novel ideas and advancing impactful projects Collaborating: Explore how generative AI can be used as a research assistant, coordinating teams and enhancing scholarly cooperation Communicating: Cautioning against over-reliance, examine how generative AI can relieve communication burdens while maintaining professionalism and etiquette Engaging: thoughtful and practical frameworks are offered for using these developments to support online engagement without sacrificing scholarly principles Mark Carrigan is a digital sociologist, author and Lecturer in Education at the University of Manchester.
Generative AI for Academics
by Mark CarriganThis is your indispensable guide to navigating the rise of generative AI as an academic. It thoughtfully explores rapidly evolving AI capabilities reshaping higher education, examining challenges and ethical dilemmas across the sector. It provides useful strategies for using generative AI in your scholarly work while upholding professional standards. This practical guidance addresses four core areas of academic work: Thinking: How to use generative AI to augment individual and collaborative scholarly thinking that can assist in developing novel ideas and advancing impactful projects Collaborating: Explore how generative AI can be used as a research assistant, coordinating teams and enhancing scholarly cooperation Communicating: Cautioning against over-reliance, examine how generative AI can relieve communication burdens while maintaining professionalism and etiquette Engaging: thoughtful and practical frameworks are offered for using these developments to support online engagement without sacrificing scholarly principles Mark Carrigan is a digital sociologist, author and Lecturer in Education at the University of Manchester.
Generative AI for Effective Software Development
by Pekka Abrahamsson Anh Nguyen-Duc Foutse KhomhThis book provides a comprehensive, empirically grounded exploration of how Generative AI is reshaping the landscape of software development. It emphasizes the empirical evaluation of Generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact. By presenting case studies, surveys, and interviews from various software development contexts, the book offers a global perspective on the integration of Generative AI, highlighting how these advanced tools are adapted to and influence diverse cultural, organizational, and technological environments. This book is structured to provide a comprehensive understanding of Generative AI and its transformative impact on the field of software engineering. The book is divided into five parts, each focusing on different aspects of Generative AI in software development. As an introduction, Part 1 presents the fundamentals of Generative AI adoption. Part 2 is a collection of empirical studies and delves into the practical aspects of integrating Generative AI tools in software engineering, with a focus on patterns, methodologies, and comparative analyses. Next, Part 3 presents case studies that showcase the application and impact of Generative AI in various software development contexts. Part 4 then examines how Generative AI is reshaping software engineering processes, from collaboration and workflow to management and agile development. Finally, Part 5 looks towards the future, exploring emerging trends, future directions, and the role of education in the context of Generative AI. The book offers diverse perspectives as it compiles research and experiences from various countries and software development environments. It also offers non-technical discussions about Generative AI in management, teamwork, business and education. This way, it is intended for both researchers in software engineering and for professionals in industry who want to learn about the impactof Generative AI on software development.
Generative AI for Web Development: Building Web Applications Powered by OpenAI APIs and Next.js
by Tom Auger Emma SaroyanExplore the world of Generative AI and understand why it matters. This book is divided into two parts, introducing tools such as ChatGPT, DALL-E, and will show you how to use them to build AI-powered web apps. The first part of the book describes Generative AI and covers the essential models and APIs from OpenAI. Legal, ethical, and security considerations are discussed to help you decide whether it is an appropriate tool for your projects. You’ll then review ChatGPT and see how to use it effectively for generating code. This is followed by a review of best practices, and tips and techniques for getting around the limitations of ChatGPT and other OpenAI APIs. The second part of the book provides practical guide to building a series of web apps with Next.js that showcase how to use the OpenAI APIs. For example, you’ll learn how to build a Story/Poetry generator, a language learning app, and a blog site with a custom Chatbot widget. The code for the web apps is generated using ChatGPT. When done with this book, you’ll have a clear understanding of Generative AI and be well on your way to building web applications powered by OpenAI APIs and Next.js. What You Will Learn Assess the legal, ethical, and security concerns with using Generative AI in web applications Review the latest APIs provided by OpenAI for generating text and image Use ChatGPT to generate code for web projects, as well as tips and tricks to working around the limitations. Who This Book Is For Experienced web developers and software engineers who know their way around HTML, CSS, and JavaScript, but have limited or no experience using Generative AI to build web applications.
Generative AI in Action (In Action)
by Amit BahreeGenerative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action!Generative AI in Action is the comprehensive and concrete guide to generative AI you&’ve been searching for. It introduces both AI&’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You&’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you&’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You&’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book&’s GitHub repository B Responsible AI tools
Generative AI in Banking Financial Services and Insurance: A Guide to Use Cases, Approaches, and Insights
by Anshul Saxena Shalaka Verma Jayant MahajanThis book explores the integration of Generative AI within the Banking, Financial Services, and Insurance (BFSI) sector, elucidating its implications, applications, and the future landscape of BFSI. The first part delves into the origins and evolution of Generative AI, providing insights into its mechanics and applications within the BFSI context. It goes into the core technologies behind Generative AI, emphasizing their significance and practical applications. The second part explores how Generative AI intersects with core banking processes, ranging from transactional activities to customer support, credit assessment, and regulatory compliance. It focuses on the digital transformation driving investment banking into the future. It also discusses AI’s role in algorithmic trading, client interactions, and regulatory adaptations. It analyzes AI-driven techniques in portfolio management, customer-centric solutions, and the next-generation approach to financial planning and advisory matters. The third part equips you with a structured roadmap for AI adoption in BFSI, highlighting the steps and the challenges. It outlines clear steps to assist BFSI institutions in incorporating Generative AI into their operations. It also raises awareness about the moral implications associated with AI in the BFSI sector. By the end of this book you will understand Generative AI’s present and future role in the BFSI sector. What You Will Learn Know what Generative AI is and its applications in the BFSI sector Understand deep learning and its significance in generative models Analyze the AI-driven techniques in portfolio management and customer-centric solutions Know the future of investment banking and trading with AI Know the challenges of integrating AI into the BFSI sector Who This Book Is For Professionals in the BFSI and IT sectors, including system administrators and programmers
Generative AI in Creative Industries (Studies in Computational Intelligence #1208)
by Tarek Gaber Khaled Shaalan Said Salloum Amina Al-Marzouqi Ra'Ed Masa'DehThis book compiles a comprehensive collection of pioneering research that addresses the multifaceted challenges and theoretical aspects of Generative AI in creative industries. Generative AI, a groundbreaking technological development, has been carving a significant niche within the creative industries. This innovative form of AI, exemplified by models like OpenAI's ChatGPT, is not only transforming the landscape of human-like text generation but is also radically reshaping creative fields such as music, literature, and visual arts. In the realm of creative industries, Generative AI serves as a catalyst for artistic innovation, offering tools for artists to push the boundaries of creativity. It assists in composing music, generating unique artworks, and even writing stories or scripts, thereby democratizing the creative process. The integration of Generative AI in these industries promises a synergy between human creativity and machine intelligence, potentially leading to novel genres and forms of art. Despite its transformative potential, the incorporation of Generative AI in creative fields is not devoid of challenges. Ethical considerations, such as authorship rights, originality, and the impact on traditional creative roles, are pivotal. Moreover, the authenticity and emotional depth of AI-generated content compared to human-created art is a subject of ongoing debate. The book aims to feature original manuscripts encompassing a wide array of topics, from the development of new generative technologies and their practical application in creative fields, to critical analysis of the ethical, legal, and cultural implications of these systems. By gathering the latest advancements in this field and spotlighting emerging avenues for research, this book serves as an invaluable resource and guide for researchers, artists, and practitioners operating at the intersection of Generative AI and creative industries.
Generative AI in Education: A Guide for Parents and Teachers
by Paolo NarcisoAs artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn. This book is a groundbreaking book that empowers parents and students to navigate this exciting new frontier, filling a critical gap in the current literature. As the first comprehensive guide to generative AI in education designed for parents and students, Generative AI in Education is positioned to become an indispensable resource. It provides the knowledge and strategies needed to effectively integrate AI into their learning journeys, transforming educational outcomes and preparing students for success in a rapidly changing world. You’ll gain a deep understanding of how tools like ChatGPT and Claude work, and how they can be leveraged to support learning across various subjects and grade levels. You’ll then see how to create clear, specific, and engaging prompts that elicit valuable responses from AI-powered tools. This book contains all the techniques for tailoring prompts to different learning objectives, styles, and contexts, and how they can use AI tools to support reading comprehension, writing skills, problem-solving, and creative thinking. What You Will Learn Apply generative AI in education Craft effective prompts for personalized learning experiences Utilize AI tools to support learning, creativity, and problem-solving Who This Book is For Parents and students who are eager to harness the power of generative AI to enhance learning experiences and prepare for success in an AI-driven future
Generative AI in FinTech: Revolutionizing Finance Through Intelligent Algorithms (Information Systems Engineering and Management #26)
by Álvaro Rocha Abhishek Bhattacharya Soumi Dutta Ambuj Kumar Agarwal Raj Gaurang TiwariThis book delves into the intersection of generative artificial intelligence (AI) and the financial Technology (FinTech) industry. This book provides a comprehensive exploration of how Generative AI, a cutting-edge subset of artificial intelligence, is fundamentally altering the landscape of finance. It meticulously unravels the intricate ways in which advanced algorithms, powered by generative AI, are transforming traditional financial processes, decision-making, risk assessment, portfolio management, fraud detection, and more. Through a detailed analysis of theoretical concepts and practical applications, we illustrate how generative AI techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are empowering FinTech applications to generate synthetic financial data, optimize trading strategies, and enhance customer experiences. Readers will gain a deep understanding of the potential of generative AI to create realistic financial scenarios, model market behaviour, and simulate various economic conditions for better planning and strategizing. Moreover, this book offers insights into ethical considerations and potential challenges associated with the use of generative AI in the FinTech domain, emphasizing the importance of responsible and accountable deployment. Additionally, Generative AI in FinTech serves as a practical guide for professionals, researchers, and enthusiasts seeking to implement generative AI solutions within the financial sector. It presents case studies and real-world examples that demonstrate the effectiveness and impact of generative AI in various FinTech applications.
Generative AI in Higher Education: Innovation Strategies for Teaching and Learning
by Abiodun Egbetokun Oluwaseun Kolade Adebowale OwoseniWith the integration of generative artificial intelligence (AI), teachers and learners now have access to powerful tools to enhance their productivity and effectiveness in their work. To meet the demands of this dynamic educational landscape, teachers must embrace AI to handle repetitive tasks, freeing them to focus on more intelligent and humanistic responsibilities. For learners, responsible use of AI could make learning more fun, personalized, flexible, and enriching. This insightful new book explores the evolving role of educators in higher education in a world of rapid technological advancements and provides a practical outline of the available technologies. By integrating Generative AI into teaching and learning, Higher Education Institutions can contribute to achieving inclusive and equitable quality education, a target of the UN Sustainable Development Goals, and promote lifelong learning opportunities for all. Generative AI can be used to enhance teaching and learning experiences, foster creativity, and develop new learning experiences in higher education. This book is a valuable resource for educators navigating the ever-changing landscape of education technology. With scientific background, practical insights and actionable tips, this book will be of interest to scholars of emerging technologies and innovation in education. It will also be of practical use to instructors seeking to harness the power of generative AI, enhancing productivity and transforming their approach to personalized learning.
Generative AI in Higher Education: The ChatGPT Effect
by Cecilia Ka Chan Tom CollotonChan and Colloton’s book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.Since November 2022, every conversation in higher education has involved ChatGPT and its impact on all aspects of teaching and learning. The book explores the necessity of AI literacy tailored to professional contexts, assess the strengths and weaknesses of incorporating ChatGPT in curriculum design, and delve into the transformation of assessment methods in the GenAI era. The authors introduce the Six Assessment Redesign Pivotal Strategies (SARPS) and an AI Assessment Integration Framework, encouraging a learner-centric assessment model. The necessity for well-crafted AI educational policies is explored, as well as a blueprint for policy formulation in academic institutions. Technical enthusiasts are catered to with a deep dive into the mechanics behind GenAI, from the history of neural networks to the latest advances and applications of GenAI technologies.With an eye on the future of AI in education, this book will appeal to educators, students and scholars interested in the wider societal implications and the transformative role of GenAI in pedagogy and research.
Generative AI in Higher Education: Volume 1 (CRC Press Reference Books in Computer Science)
by Emmanuel K NarteyDivided into two volumes, this book develops guiding principles for higher education institutions to use GenAI effectively and ethically in teaching and learning, articulating a roadmap for implementation at institutional levels and addressing the conundrum of using GenAI in higher education. As higher education institutions take different attitudes and approaches to Generative AI (GenAI), with some viewing it as a threat to academic integrity and therefore banning its use, while others have embraced it as an innovation to academic practice and have implemented guidance on how to use it ethically, this book makes clear that GenAI, such as ChatGPT, is not the problem itself; the issue is how we engage with it.
Generative AI in e-Business: 22nd Workshop on e-Business, WeB 2023, Hyderabad, India, December 9, 2023, Revised Selected Papers (Lecture Notes in Business Information Processing #525)
by Bin Zhu Abhishek Kathuria Prasanna P. Karhade Ria SonpatkiThis book constitutes revised selected papers from the 22nd Workshop on e-Business, WeB 2023, which took place in Hyderabad, India, on December 9, 2023. The purpose of WeB is to provide a forum for researchers and practitioners to discuss findings, novel ideas, and lessons learned to address major challenges and map out the future directions for e-Business. The WeB 2023 theme was “Generative AI in e-Business”. The 13 full papers included in this volume were carefully reviewed and selected from a total of 46 submissions. They focus on both the transformative potential and the challenges of integrating generative AI into e-business models, paving the way for a future where AI empowers businesses and enriches lives.
Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications
by Chris Fregly Antje Barth Shelbee EigenbrodeCompanies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.Apply generative AI to your business use casesDetermine which generative AI models are best suited to your task Perform prompt engineering and in-context learningFine-tune generative AI models on your datasets with low-rank adaptation (LoRA)Align generative AI models to human values with reinforcement learning from human feedback (RLHF)Augment your model with retrieval-augmented generation (RAG)Explore libraries such as LangChain and ReAct to develop agents and actionsBuild generative AI applications with Amazon Bedrock
Generative AI with Amazon Bedrock: Build, scale, and secure generative AI applications using Amazon Bedrock
by Bunny Kaushik Shikhar KwatraBecome proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standardsKey FeaturesLearn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist ArchitectsMaster the core techniques to develop and deploy several AI applications at scaleGo beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learnExplore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performanceExplore several architecture patterns for different business use casesGain insights into ethical AI practices, model governance, and risk mitigation strategiesEnhance your skills in employing agents to develop intelligence and orchestrate tasksMonitor and understand metrics and Amazon Bedrock model responseExplore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standardsWho this book is forThis book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.
Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
by Ben Auffarth2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key FeaturesLearn how to leverage LangChain to work around LLMs’ inherent weaknessesDelve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challengesGet better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into realityBook DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learnCreate LLM apps with LangChain, like question-answering systems and chatbotsUnderstand transformer models and attention mechanismsAutomate data analysis and visualization using pandas and PythonGrasp prompt engineering to improve performanceFine-tune LLMs and get to know the tools to unleash their powerDeploy LLMs as a service with LangChain and apply evaluation strategiesPrivately interact with documents using open-source LLMs to prevent data leaksWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.
Generative AI with LangChain: Build production ready LLM applications and advanced agents using Python and LangGraph
by Ben Auffarth Leonid KuliginBeyond foundational LangChain documentation and LangGraph interfaces, learn enterprise patterns, key design pattern to build AI agents, battle-tested strategies, and proven architectures used in production. Ideal for Python developers building generative AI at scale. Key FeaturesGet to grips with building AI agents with LangGraphLearn about enterprise-grade testing, observability, and LLM evaluation frameworksCover RAG implementation with cutting-edge retrieval strategies and new reliability techniquesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis revised edition builds a foundation in agentic AI, LLM fundamentals, LangChain, & LangGraph for developers at all levels. Fully updated to cover the latest in LangChain and production LLM applications, it captures the evolving ecosystem and enterprise deployment landscape. New coverage includes multi-agent architectures, LangGraph interfaces, robust RAG techniques with hybrid search, re-rankers, and advanced fact-checking mechanisms, plus enterprise-grade testing frameworks. It provides coverage of key design patterns behind agentic systems, practical implementations of multi-agent systems for complex tasks. Explore cutting-edge agent strategies such as Tree of Thought, multi-agent orchestration, detailed error handling, and structured output generation. Coverage dedicated to evaluation, testing, and production deployment reflect the maturing LLM application landscape. Design secure, compliant AI systems with built-in production safeguards, responsible development practices, and a perspective on future research directions.The enhanced RAG coverage features techniques like hybrid search, re-rankers, and fact-checking mechanisms. Whether upgrading existing LLM applications or building new enterprise-scale solutions, by the end of the book, you will have updated knowledge on the practical patterns needed for production successWhat you will learnDesign and implement refined multi-agent systems using LangGraph Enterprise-grade testing and evaluation frameworks for LLM applications Deploy production-ready observability and monitoring solutions Build RAG systems with hybrid search and re-ranking capabilities Implement agents for software development and data analysis Work with latest LLMs and providers Google Gemini, Anthropic and Mistral, DeepSeek, and OpenAI o3-miniOptimize cost and performance across different deployment typesDesign secure, compliant AI systems with current best practicesWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain and LangGraph, wanting to build production-ready LLM applications. This book emphasizes on enterprise deployment patterns, making it especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this version also caters to engineering teams and decision-makers implementing enterprise-wide LLM strategies. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.
Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications
by Raghav Bali Joseph BabcockMaster GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.What you will learnGrasp the core concepts and capabilities of LLMsCraft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputsUnderstand how attention and transformers have changed NLPOptimize your diffusion models by combining them with VAEsBuild text generation pipelines based on LSTMs and LLMsLeverage the power of open-source LLMs, such as Llama and Mistral, for diverse applicationsWho this book is forThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.
Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
by Joseph Babcock Raghav BaliFun and exciting projects to learn what artificial minds can createKey FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that's under the hood and uncover surprising links between text, image, and music generation.What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.