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A practical syllabus-based UiPath Associate certification guide to confidently passing your UiPath RPA Associate certification examination, covering real-world business cases, mock tests, and moreKey FeaturesGet certified successfully with this detailed syllabus-oriented examination guideWork with real-world business cases that will help you grasp RPA topics better using practical examplesPractice with quizzes and mock exam papers with answers to prepare for the exam confidentlyBook DescriptionUiPath is the most popular vendor in the Robotic Process Automation (RPA) industry. If you're an RPA enthusiast or citizen developer who wants to succeed in the industry, achieving this certification can help you get accredited and ready for real-world challenges using UiPath.UiPath Associate Certification Guide offers complete, up-to-date coverage of the UiPath RPA Associate certification exam to help you pass on the first attempt and get certified. The book is written in a clear, succinct way with self-assessment questions, quizzes with answers at the end of each chapter, exam tips, and mock exams with detailed answers and explanations. You'll start by getting to grips with the basic concepts of UiPath RPA, and then progress to an in-depth discussion of all the concepts required for Associate certification. Finally, you'll develop UiPath skills by gaining the required knowledge and implement these skills using sample business cases.By the end of this UiPath book, you'll have covered everything you need to pass the exam, gained the knowledge you need to work on real-world case studies, and learned how to apply the various concepts to build enterprise-level use cases.What you will learnGet to grips with the basic concepts of UiPath RPAEquip yourself with every resource required to pass the Associate exam on the first attemptGain the ability to apply what you've learned using real-world use casesWork on different use cases and test your knowledge by taking quizzesGet a feel for the real exam with practice questions and mock examsUnderstand the concepts used in building enterprise-level use casesWho this book is forThis RPA book is for those in technical and semi-technical roles such as citizen RPA developers, junior RPA developers, RPA developers, solution architects, business analysts, system administrators, college or university students and graduates, and UiPath and partner employees in pre-sales, services, support, and so on. If you are looking to get certified and meet the industry standard with the largest RPA vendor, then this book is for you.

Discover powerful ways to use deep learning algorithms and solve real-world computer vision problems using Python Key Features Solve the trickiest of problems in computer vision by combining the power of deep learning and neural networks Leverage PyTorch 1.x capabilities to perform image classification, object detection, and more Train and deploy enterprise-grade, deep learning models for computer vision applications Book Description Computer vision techniques play an integral role in helping developers gain a high-level understanding of digital images and videos. With this book, you'll learn how to solve the trickiest problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of CV tasks. Starting with a quick overview of the PyTorch library and key deep learning concepts, the book then covers common and not-so-common challenges faced while performing image recognition, image segmentation, object detection, image generation, and other tasks. Next, you'll understand how to implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you'll learn how to solve any issue you might face while fine-tuning the performance of a model or integrating it into your application. Later, you'll get to grips with scaling your model to handle larger workloads, and implementing best practices for training models efficiently. By the end of this CV book, you'll be proficient in confidently solving many CV related problems using deep learning and PyTorch. What you will learn Develop, train and deploy deep learning algorithms using PyTorch 1.x Understand how to fine-tune and change hyperparameters to train deep learning algorithms Perform various CV tasks such as classification, detection, and segmentation Implement a neural style transfer network based on CNNs and pre-trained models Generate new images and implement adversarial attacks using GANs Implement video classification models based on RNN, LSTM, and 3D-CNN Discover best practices for training and deploying deep learning algorithms for CV applications Who this book is for Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate-level knowledge of computer vision concepts, along with Python programming experience is required.Michael Avendi is a principal data scientist with vast experience in deep learning, computer vision, and medical imaging analysis. He works on the research and development of data-driven algorithms for various imaging problems, including medical imaging applications. His research papers have been published in major medical journals, including the Medical Imaging Analysis journal. Michael Avendi is an active Kaggle participant and was awarded a top prize in a Kaggle competition in 2017.

Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAIKey FeaturesLeverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI modelsUse LLM-powered agents for custom tasks and real-world interactionsGain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionHarness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You'll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you'll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You'll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust and transparency in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.What you will learnUnderstand fundamental NLP concepts along with their applications using examples in PythonClassify text quickly and accurately with rule-based and supervised methodsTrain NER models and perform sentiment analysis to identify entities and emotions in textExplore topic modeling and text visualization to reveal themes and relationships within textLeverage Hugging Face and OpenAI LLMs to perform advanced NLP tasksUse question-answering techniques to handle both open and closed domainsApply XAI techniques to better understand your model predictionsWho this book is forThis updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you're looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

Create engaging AI experiences by mastering ChatGPT for business and leveraging user interface design practices, research methods, prompt engineering, the feeding lifecycle, and moreKey FeaturesLearn in-demand design thinking and user research techniques applicable to all conversational AI platformsMeasure the quality and evaluate ChatGPT from a customer's perspective for optimal user experienceSet up and use your secure private data, documents, and materials to enhance your ChatGPT modelsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMany enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach. UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience's voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You'll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions. By the end, you'll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization's use of ChatGPT.What you will learnAlign with user needs by applying design thinking to tailor ChatGPT to meet customer expectationsHarness user research to enhance chatbots and recommendation enginesTrack quality metrics and learn methods to evaluate and monitor ChatGPT's quality and usabilityEstablish and maintain a uniform style and tone with prompt engineering and fine-tuningApply proven heuristics by monitoring and assessing the UX for conversational experiences with trusted methodsRefine continuously by implementing an ongoing process for chatbot and feedingWho this book is forThis book is for user experience designers, product managers, and product owners of business and enterprise ChatGPT solutions who are interested in learning how to design and implement ChatGPT-4 solutions for enterprise needs. You should have a basic-to-intermediate level of understanding in UI/UX design concepts and fundamental knowledge of ChatGPT-4 and its capabilities.

This book covers current AI applications and techniques for solving problems and accomplishing tasks. It introduces branches of AI such as formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic. It emphasizes expert systems, with sections on state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog.It begins with an introduction to AI and its applications, setting the stage for foundational concepts. Readers are guided through state space search and heuristic search strategies, crucial for problem-solving in AI. The focus shifts to expert systems, covering their development life cycle, knowledge acquisition, and representation, providing a deep dive into emulating human decision-making.Later chapters cover neural networks and the learning process, essential for creating adaptive systems. Sections on fuzzy logic and fuzzy systems introduce methods for handling uncertainty in AI. Final chapters on programming in logic and advanced Prolog offer practical techniques for AI solutions. This approach equips readers with the skills to apply AI in various domains, enhancing their problem-solving abilities and understanding of intelligent systems.

This third edition provides a comprehensive, accessible presentation of AI, including examples, applications, full-color images, and human interest boxes. New chapters on deep learning, AI security, and AI programming keep the content cutting-edge. Topics like neural networks, genetic algorithms, natural language processing, planning, and complex board games are covered.The course starts with an AI overview, moving through uninformed search, intelligent search methods, and game-based strategies. It delves into logic in AI, knowledge representation, production systems, uncertainty in AI, and expert systems. Middle chapters cover machine learning, neural networks, and deep learning. It continues with nature-inspired search methods, natural language processing, and automated planning, ending with robotics and advanced computer games.These AI concepts are crucial for developing sophisticated AI applications. This book transitions you from novice to proficient AI practitioner, equipped with practical skills and comprehensive knowledge. Companion files with resources, simulations, and figures enhance learning. By the end, you'll understand AI principles and applications, ready to tackle real-world challenges.

Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You'll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you'll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you'll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learnDiscover the fundamentals of GenAI and its foundations in NLPDissect foundational generative architectures including GANs, transformers, and diffusion modelsFind out how to fine-tune LLMs for specific NLP tasksUnderstand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as financeExplore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAGImplement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputsWho this book is forThis book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.

Unlock the power of GenAI by effortlessly linking your C# and Python apps with cutting-edge models, orchestrating diverse AI services with finesse, and crafting bespoke applications through immersive, real-world examplesKey FeaturesLink your C# and Python applications with the latest AI models from OpenAICombine and orchestrate different AI services such as text and image generatorsCreate your own AI apps with real-world use case examples that show you how to use basic generative AI, create images, process documents, use a vector databasePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the fast-paced world of AI, developers are constantly seeking efficient ways to integrate AI capabilities into their apps. Microsoft Semantic Kernel simplifies this process by using the GenAI features from Microsoft and OpenAI. Written by Lucas A. Meyer, a Principal Research Scientist in Microsoft's AI for Good Lab, this book helps you get hands on with Semantic Kernel. It begins by introducing you to different generative AI services such as GPT-3.5 and GPT-4, demonstrating their integration with Semantic Kernel. You'll then learn to craft prompt templates for reuse across various AI services and variables. Next, you'll learn how to add functionality to Semantic Kernel by creating your own plugins. The second part of the book shows you how to combine multiple plugins to execute complex actions, and how to let Semantic Kernel use its own AI to solve complex problems by calling plugins, including the ones made by you. The book concludes by teaching you how to use vector databases to expand the memory of your AI services and how to help AI remember the context of earlier requests. You'll also be guided through several real-world examples of applications, such as RAG and custom GPT agents. By the end of this book, you'll have gained the knowledge you need to start using Semantic Kernel to add AI capabilities to your applications.What you will learnWrite reusable AI prompts and connect to different AI providersCreate new plugins that extend the capabilities of AI servicesUnderstand how to combine multiple plugins to execute complex actionsOrchestrate multiple AI services to accomplish a taskLeverage the powerful planner to automatically create appropriate AI callsUse vector databases as additional memory for your AI tasksDeploy your application to ChatGPT, making it available to hundreds of millions of usersWho this book is forThis book is for beginner-level to experienced.NET or Python software developers who want to quickly incorporate the latest AI technologies into their applications, without having to learn the details of every new AI service. Product managers with some development experience will find this book helpful while creating proof-of-concept applications. This book requires working knowledge of programming basics.

Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key FeaturesLearn how to wrangle different types of datasets and analytics problems into networksLeverage graph theoretic algorithms to analyze data efficientlyApply the skills you gain to solve a variety of problems through case studies in PythonPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You'll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you'll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you'll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you'll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learnTransform different data types, such as spatial data, into network formatsExplore common network science tools in PythonDiscover how geometry impacts spreading processes on networksImplement machine learning algorithms on network data featuresBuild and query graph databasesExplore new frontiers in network science such as quantum algorithmsWho this book is forIf you're a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

The term'4th Industrial Revolution'is often mentioned in the media, but public understanding of its technologies lags behind their rapid development. This book bridges the gap, explaining essential technologies like IoT, blockchain, AI, cloud computing, and big data. It aims to enhance comprehension by minimizing technical content. The book introduces key technologies and their applications, emphasizing their importance in contemporary business models. Readers will find accessible descriptions and practical examples to aid understanding. Covering topics and trends vital for modern business, this book ensures readers grasp the technological landscape shaping the future. Throughout the book, you'll explore how these technologies are revolutionizing industries and their integration into business strategies. This journey provides a comprehensive understanding of the 4th Industrial Revolution, equipping readers with the knowledge needed to navigate and leverage these advancements effectively.

This book offers an in-depth exploration of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Beginning with foundational concepts like the attention mechanism and tokenization techniques, it delves into the intricacies of Transformer and BERT architectures. Advanced topics cover the latest developments in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the nuances of Transformer architecture, a detailed exploration of the BERT family, and hands-on guidance on working with GPT-3. The journey continues with a comprehensive overview of ChatGPT, GPT-4, and visualization using generative AI. The book also discusses influential AI organizations such as DeepMind, OpenAI, Cohere, and Hugging Face. Readers will gain a thorough understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Companion files with numerous code samples and figures from the book enhance the learning experience, providing practical tools and resources. This book is an essential guide for those seeking to master the latest advancements in natural language processing and generative AI.

From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key FeaturesExplore AI's role in web development, from shaping projects to architecting solutionsMaster advanced AI strategies to build cutting-edge applicationsAnticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web developmentPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIf you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience. The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape. Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain.What you will learnBuild AI-powered web projects with optimized modelsPersonalize UX dynamically with AI, NLP, chatbots, and recommendationsExplore AI coding assistants and other tools for advanced web developmentCraft data-driven, personalized experiences using pattern recognitionArchitect effective AI solutions while exploring the future of web developmentBuild secure and ethical AI applications following TRiSM best practicesExplore cutting-edge AI and web development trendsWho this book is forThis book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts.

Explore ChatGPT technologies to create state-of-the-art chatbots and voice assistants, and prepare to lead the AI revolutionKey FeaturesLearn how to leverage ChatGPT to create innovative conversational AI solutions for your organizationHarness LangChain and delve into step-by-step LLM application development for conversational AIGain insights into security, privacy, and the future landscape of large language models and conversational AIPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You'll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you'll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you'll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You'll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you'll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.What you will learnGain a solid understanding of ChatGPT and its capabilities and limitationsUnderstand how to use ChatGPT for conversation designDiscover how to use advanced LangChain techniques, such as prompting, memory, agents, chains, vector stores, and toolsCreate a ChatGPT chatbot that can answer questions about your own dataDevelop a chatbot powered by ChatGPT APIExplore the future of conversational AI, LLMs, and ChatGPT alternativesWho this book is forThis book is for tech-savvy readers, conversational AI practitioners, engineers, product owners, business analysts, and entrepreneurs wanting to integrate ChatGPT into conversational experiences and explore the possibilities of this game-changing technology. Anyone curious about using internal data with ChatGPT and looking to stay up to date with the developments in large language models will also find this book helpful. Some expertise in coding and standard web design concepts would be useful, along with familiarity with conversational AI terminology, though not essential.

Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key FeaturesMaster the concepts of statistics and ML to interpret models and guide decisionsIdentify valuable AI use cases and manage data science projects from start to finishEmpower top data science teams to solve complex problems and build AI productsPurchase of the print Kindle book includes a free PDF eBookBook DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you'll be able to characterize the data within your organization and frame business problems as data science problems.What you will learnDiscover how to interpret common statistical quantities and make data-driven decisionsExplore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learningFind out how to evaluate statistical and machine learning modelsUnderstand the data science lifecycle, from development to monitoring of models in productionKnow when to use ML, statistical modeling, or traditional BI methodsManage data teams and data science projects effectivelyWho this book is forThis book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.

Embark on a journey through AI with our beginner-friendly course, empowering you to understand and ethically apply AI, shaping the future of technology and innovation.Key FeaturesSimplify AI concepts for easy understandingHighlight ethical AI use and privacyForesee the influence of AI on future workBook DescriptionThe course begins with an engaging introduction to the world of Artificial Intelligence, making it approachable for absolute beginners. We unravel the mysteries of AI's evolution, from its historical roots to the cutting-edge technologies shaping our future. By explaining complex concepts in simple terms, this course aims to illuminate the path for those curious about how AI impacts our world. The course focuses on the core components of AI, including machine learning, deep learning, and natural language processing, before advancing to more specialized topics like generative AI and computer vision. Each module is designed to build a comprehensive understanding, emphasizing why these technologies are crucial for solving real-world problems and how they're transforming industries. The course wraps up by exploring the ethical considerations and privacy concerns associated with AI, along with a visionary look at the future of work in an AI-driven world. It offers a treasure trove of further resources, ensuring learners have everything they need to continue their exploration of AI.What you will learnDecode the history and evolution of AINavigate through AI's development stagesGrasp natural language processing fundamentalsDive into generative AI and its applicationsUnderstand recommender systems and computer visionRecognize AI's impact on privacy and ethicsWho this book is forThis course is ideal for enthusiasts and professionals eager to understand AI from the ground up. No prior knowledge is required, making this course perfect for those looking to get acquainted with AI concepts and their applications across various industries.

Explore the vast possibilities of integrating the ChatGPT API across various domains, from creating simple wrappers to developing knowledge-based assistants, multi-model applications, and conversational interfacesKey FeaturesUnderstand the different elements, endpoints, and parameters of the OpenAI APIBuild tailored intelligent applications and workflows with the OpenAI APICreate versatile assistants with for a multitude of tasksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs artificial intelligence continues to reshape industries with OpenAI at the forefront of AI research, knowing how to create innovative applications such as chatbots, virtual assistants, content generators, and productivity enhancers is a game-changer. This book takes a practical, recipe-based approach to unlocking the power of OpenAI API to build high-performance intelligent applications in diverse industries and seamlessly integrate ChatGPT in your workflows to increase productivity. You'll begin with the OpenAI API fundamentals, covering setup, authentication, and key parameters, and quickly progress to the different elements of the OpenAI API. Once you've learned how to use it effectively and tweak parameters for better results, you'll follow advanced recipes for enhancing user experience and refining outputs. The book guides your transition from development to live application deployment, setting up the API for public use and application backend. Further, you'll discover step-by-step recipes for building knowledge-based assistants and multi-model applications tailored to your specific needs. By the end of this book, you'll have worked through recipes involving various OpenAI API endpoints and built a variety of intelligent applications, ready to apply this experience to building AI-powered solutions of your own.What you will learnGrasp the fundamentals of the OpenAI APINavigate the API's capabilities and limitations of the APISet up the OpenAI API with step-by-step instructions, from obtaining your API key to making your first callExplore advanced features such as system messages, fine-tuning, and the effects of different parametersIntegrate the OpenAI API into existing applications and workflows to enhance their functionality with AIDesign and build applications that fully harness the power of ChatGPTWho this book is forThis book is perfect for developers, data scientists, AI/tech enthusiasts, citizen developers, and no-code aficionados keen on using and mastering the OpenAI API. Whether you're a beginner or experienced professional, this book is ideal for quickly creating intelligent applications such as chatbots or content generators, through step-by-step recipes that take you from the basics of the API to creating sophisticated applications systematically. The OpenAI API is accessed with Python in this book, so familiarity with Python and APIs is preferred but not mandatory.

Enhance your writing with AI by mastering prompt engineering techniques and become an expert in developing and utilizing LLM prompts across applicationsKey FeaturesMaster prompt engineering techniques to harness AI's writing potentialDiscover diverse LLM applications for content creation and beyondLearn through practical examples, use cases, and hands-on guidancePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnlocking the Secrets of Prompt Engineering is your key to mastering the art of AI-driven writing. This book propels you into the world of large language models (LLMs), empowering you to create and apply prompts effectively for diverse applications, from revolutionizing content creation and chatbots to coding assistance. Starting with the fundamentals of prompt engineering, this guide provides a solid foundation in LLM prompts, their components, and applications. Through practical examples and use cases, you'll discover how LLMs can be used for generating product descriptions, personalized emails, social media posts, and even creative writing projects like fiction and poetry. The book covers advanced use cases such as creating and promoting podcasts, integrating LLMs with other tools, and using AI for chatbot development. But that's not all. You'll also delve into the ethical considerations, best practices, and limitations of using LLM prompts as you experiment and optimize your approach for best results. By the end of this book, you'll have unlocked the full potential of AI in writing and content creation to generate ideas, overcome writer's block, boost productivity, and improve communication skills.What you will learnExplore the different types of prompts, their strengths, and weaknessesUnderstand the AI agent's knowledge and mental modelEnhance your creative writing with AI insights for fiction and poetryDevelop advanced skills in AI chatbot creation and deploymentDiscover how AI will transform industries such as education, legal, and othersIntegrate LLMs with various tools to boost productivityUnderstand AI ethics and best practices, and navigate limitations effectivelyExperiment and optimize AI techniques for best resultsWho this book is forThis book is for a wide audience, including writers, marketing and business professionals, researchers, students, tech enthusiasts, and creative individuals. Anyone looking for strategies and examples for using AI co-writing tools like ChatGPT effectively in domains such as content creation, drafting emails, and inspiring artistic works, will find this book especially useful. If you are interested in AI, NLP, and innovative software for personal or professional use, this is the book for you.

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesKey FeaturesBuild and refine LLMs step by step, covering data preparation, RAG, and fine-tuningLearn essential skills for deploying and monitoring LLMs, ensuring optimal performance in productionUtilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applicationsBook DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that's cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine it with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoringPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsDesign scalable and modularLLM systemsLearn about RAG applications by building a feature and inference pipelineWho this book is forThis book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios

Develop your data science skills with Apache Spark to solve real-world problems for Fortune 500 companies using scalable algorithms on large cloud computing clustersKey FeaturesApply techniques to analyze big data and uncover valuable insights for machine learningLearn to use cloud computing clusters for training machine learning models on large datasetsDiscover practical strategies to overcome challenges in model training, deployment, and optimizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark's capabilities for efficient data processing, model building, and optimization. With Deepak's expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes. This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You'll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks. By the end of this book, you'll be ready to handle big data and create advanced machine learning models with Apache Spark.What you will learnMaster Apache Spark for efficient, large-scale data processing and analysisUnderstand core machine learning concepts and their applications with SparkImplement data preprocessing techniques for feature extraction and transformationExplore supervised learning methods – regression and classification algorithmsApply unsupervised learning for clustering tasks and recommendation systemsDiscover frequent pattern mining techniques to uncover data trendsWho this book is forThis book is ideal for data scientists, ML engineers, data engineers, students, and researchers who want to deepen their knowledge of Apache Spark's tools and algorithms. It's a must-have for those struggling to scale models for real-world problems and a valuable resource for preparing for interviews at Fortune 500 companies, focusing on large dataset analysis, model training, and deployment.

Learn from real-world examples how leveraging AI, including machine learning and generative AI, is imperative for businesses to navigate risk, drive value, and gain a competitive advantageKey FeaturesUnderstand machine learning and generative AI terminology, concepts, and the AI technology stack.Learn from diverse real-world case studies narrated by business leaders in their own voice.Apply a value-driven approach to AI applications across multiple business sectors.Book DescriptionBusiness leaders are challenged by the speed of AI innovation and how to navigate disruption and uncertainty. This book is a crucial resource for those who want to understand how to leverage AI to drive business value, drawn from the firsthand experience of those who have been implementing this technology successfully. The AI Value Playbook focuses on questions frequently posed by leaders and boards. How can businesses adapt to these emerging technologies? How can they start building and deploying AI as a strategic asset to drive efficiency? What risks or threats need to be considered? How quickly can value be created? This book is a response to those demands. In a series of in-depth and wide-ranging conversations with practitioners, from CEOs leading new generative AI-based companies to Data Scientists and CFOs working in more traditional companies. Our experts share their hard-earned wisdom, talking candidly about their successes and failures, and what excites them about the future. These interviews offer unique insights for business leaders to apply to their own organizations. The book distils a value-driven playbook for how AI can be put to work today.What you will learnFundamentals of AI concepts and the tech stackHow AI works with real-world practical applicationsHow to integrate into your company's overall strategyHow to incorporate generative AI in your processesHow to drive value with sector-wide examplesHow to organize an AI-driven operating modelHow to use AI for competitive advantageThe dos and don'ts of AI applicationWho this book is forThe AI Value Playbook is aimed at supporting non-technical executives and board members to quickly formulate a perspective on how to integrate AI. This book addresses the gap in data and AI knowledge in leadership teams that have an appetite for nuanced, targeted and practical solutions. It includes which levers and processes to consider to future-proof their business. It speaks to an audience interested in understanding how AI can drive value for their organisations.

Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python librariesKey FeaturesLearn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPyTake advantage of cloud computing technology to increase the performance of your solutionsDiscover bio-inspired algorithms such as particle swarm optimization (PSO) and NEATPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you'll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you'll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You'll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you'll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learnUse genetic algorithms to solve planning, scheduling, gaming, and analytics problemsCreate reinforcement learning, NLP, and explainable AI applicationsEnhance the performance of ML models and optimize deep learning architectureDeploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiencyExplore how images can be reconstructed using a set of semi-transparent shapesDelve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversityWho this book is forIf you're a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.

Explore transformer-based language models from BERT to GPT, delving into NLP and computer vision tasks, while tackling challenges effectivelyKey FeaturesUnderstand the complexity of deep learning architecture and transformers architectureCreate solutions to industrial natural language processing (NLP) and computer vision (CV) problemsExplore challenges in the preparation process, such as problem and language-specific dataset transformationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTransformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You'll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you'll focus on using vision transformers to solve computer vision problems. Finally, you'll discover how to harness the power of transformers to model time series data and for predicting. By the end of this transformers book, you'll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.What you will learnFocus on solving simple-to-complex NLP problems with PythonDiscover how to solve classification/regression problems with traditional NLP approachesTrain a language model and explore how to fine-tune models to the downstream tasksUnderstand how to use transformers for generative AI and computer vision tasksBuild transformer-based NLP apps with the Python transformers libraryFocus on language generation such as machine translation and conversational AI in any languageSpeed up transformer model inference to reduce latencyWho this book is forThis book is for deep learning researchers, hands-on practitioners, and ML/NLP researchers. Educators, as well as students who have a good command of programming subjects, knowledge in the field of machine learning and artificial intelligence, and who want to develop apps in the field of NLP as well as multimodal tasks will also benefit from this book's hands-on approach. Knowledge of Python (or any programming language) and machine learning literature, as well as a basic understanding of computer science, are required.

Learn about LangChain and LLMs with'LangChain in your Pocket,'a comprehensive guide to leveraging this innovative framework for building language-based applications.Key FeaturesStep-by-step code explanations with expected outputs for each solutionPractical examples and hands-on tutorials for real-world applicationDetailed discussions on managing and evaluating large language modelsBook Description'LangChain in your Pocket'offers a detailed exploration into the LangChain framework, designed to enhance your skills in developing sophisticated language understanding models and applications. This book begins with the basics, introducing you to the fundamental concepts of LangChain through a simple'Hello World'example. As you progress, you'll delve into various LangChain modules, learning how to create agents, manage memory, and utilize output parsers effectively. The journey continues as you explore the RAG Framework, vector databases, and their applications in natural language processing, providing you with the tools to tackle common NLP problems efficiently. The book also addresses critical aspects of working with large language models (LLMs), such as prompt engineering, handling hallucinations, and evaluating model outputs. Advanced topics like autonomous AI agents and the integration of LangSmith and LangServe are covered, giving you a holistic view of what you can achieve with LangChain. By the end of this book, you will not only understand the technical aspects of LangChain but also how to apply these principles in real-world scenarios, making it an essential resource for anyone looking to advance their capabilities in AI and language processing.What you will learnNavigate the basic to advanced features of LangChainBuild and manage language understanding models and applicationsEmploy advanced prompt engineering techniquesImplement and evaluate large language models effectivelyDevelop autonomous AI agents with LangChainIntegrate LangSmith and LangServe for enhanced functionalityWho this book is forThe book'LangChain in your Pocket: Beginner's Guide to Building Generative AI Applications using LLMs'is an excellent resource for individuals new to the world of Generative AI. Whether you are a software developer, data scientist, or student, this beginner-friendly book provides a comprehensive introduction to the LangChain framework and its practical applications. Regardless of your prior experience, this book is a valuable asset for anyone interested in diving into the world of Generative AI and leveraging the power of LangChain.

Master automatic speech recognition (ASR) with groundbreaking generative AI for unrivaled accuracy and versatility in audio processing Key FeaturesUncover the intricate architecture and mechanics behind Whisper's robust speech recognitionApply Whisper's technology in innovative projects, from audio transcription to voice synthesisNavigate the practical use of Whisper in real-world scenarios for achieving dynamic tech solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs the field of generative AI evolves, so does the demand for intelligent systems that can understand human speech. Navigating the complexities of automatic speech recognition (ASR) technology is a significant challenge for many professionals. This book offers a comprehensive solution that guides you through OpenAI's advanced ASR system. You'll begin your journey with Whisper's foundational concepts, gradually progressing to its sophisticated functionalities. Next, you'll explore the transformer model, understand its multilingual capabilities, and grasp training techniques using weak supervision. The book helps you customize Whisper for different contexts and optimize its performance for specific needs. You'll also focus on the vast potential of Whisper in real-world scenarios, including its transcription services, voice-based search, and the ability to enhance customer engagement. Advanced chapters delve into voice synthesis and diarization while addressing ethical considerations. By the end of this book, you'll have an understanding of ASR technology and have the skills to implement Whisper. Moreover, Python coding examples will equip you to apply ASR technologies in your projects as well as prepare you to tackle challenges and seize opportunities in the rapidly evolving world of voice recognition and processing.What you will learnIntegrate Whisper into voice assistants and chatbotsUse Whisper for efficient, accurate transcription servicesUnderstand Whisper's transformer model structure and nuancesFine-tune Whisper for specific language requirements globallyImplement Whisper in real-time translation scenariosExplore voice synthesis capabilities using Whisper's robust techExecute voice diarization with Whisper and NVIDIA's NeMoNavigate ethical considerations in advanced voice technologyWho this book is forLearn OpenAI Whisper is designed for a diverse audience, including AI engineers, tech professionals, and students. It's ideal for those with a basic understanding of machine learning and Python programming, and an interest in voice technology, from developers integrating ASR in applications to researchers exploring the cutting-edge possibilities in artificial intelligence.

Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key FeaturesLearn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPTMaster embedding techniques and machine learning principles for real-world applicationsUnderstand the mathematical foundations of NLP and deep learning designsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDo you want to master Natural Language Processing (NLP) but don't know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you'll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You'll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You'll also explore general machine learning techniques and find out how they relate to NLP. Next, you'll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You'll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs'theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You'll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learnMaster the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in PythonModel and classify text using traditional machine learning and deep learning methodsUnderstand the theory and design of LLMs and their implementation for various applications in AIExplore NLP insights, trends, and expert opinions on its future direction and potentialWho this book is forThis book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.

Master ChatGPT and the OpenAI API and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.Key FeaturesEnhance your skills by leveraging ChatGPT to generate complex commands, write code, and create toolsAutomate penetration testing, risk assessment, and threat detection tasks using the OpenAI API and Python programmingRevolutionize your approach to cybersecurity with an AI-powered toolkitPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAre you ready to unleash the potential of AI-driven cybersecurity? This cookbook takes you on a journey toward enhancing your cybersecurity skills, whether you're a novice or a seasoned professional. By leveraging cutting-edge generative AI and large language models such as ChatGPT, you'll gain a competitive advantage in the ever-evolving cybersecurity landscape. ChatGPT for Cybersecurity Cookbook shows you how to automate and optimize various cybersecurity tasks, including penetration testing, vulnerability assessments, risk assessment, and threat detection. Each recipe demonstrates step by step how to utilize ChatGPT and the OpenAI API to generate complex commands, write code, and even create complete tools. You'll discover how AI-powered cybersecurity can revolutionize your approach to security, providing you with new strategies and techniques for tackling challenges. As you progress, you'll dive into detailed recipes covering attack vector automation, vulnerability scanning, GPT-assisted code analysis, and more. By learning to harness the power of generative AI, you'll not only expand your skillset but also increase your efficiency. By the end of this cybersecurity book, you'll have the confidence and knowledge you need to stay ahead of the curve, mastering the latest generative AI tools and techniques in cybersecurity.What you will learnMaster ChatGPT prompt engineering for complex cybersecurity tasksUse the OpenAI API to enhance and automate penetration testingImplement artificial intelligence-driven vulnerability assessments and risk analysesAutomate threat detection with the OpenAI APIDevelop custom AI-enhanced cybersecurity tools and scriptsPerform AI-powered cybersecurity training and exercisesOptimize cybersecurity workflows using generative AI-powered techniquesWho this book is forThis book is for cybersecurity professionals, IT experts, and enthusiasts looking to harness the power of ChatGPT and the OpenAI API in their cybersecurity operations. Whether you're a red teamer, blue teamer, or security researcher, this book will help you revolutionize your approach to cybersecurity with generative AI-powered techniques. A basic understanding of cybersecurity concepts along with familiarity in Python programming is expected. Experience with command-line tools and basic knowledge of networking concepts and web technologies is also required.

Build scalable, secure, and intelligent systems by utilizing IoT architectures, AWS, Azure, AI, and real-world solutions to become a skilled IIoT architectKey FeaturesLeverage IoT, AI/ML, and cloud technologies to unlock industrial potential and drive business innovationWork with labs on real-world edge computing scenarios, integrating AWS, Azure, and open source toolsUse diagnostic and predictive analytics to develop digital twins, improve industrial processes, and manage assetsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn today's automation-driven era, precision is crucial, and the Industrial Internet of Things (IIoT) has made a remarkable impact. This updated second edition explores the technologies fueling the IIoT revolution and shares essential knowledge to enable you to establish remote-access networks. Written by IIoT and AI experts, as well as renowned authors, this book helps you enhance your skills in emerging technologies by introducing new techniques from Azure and AWS and keeping you up to date with the latest advancements. You'll find out how Artificial Intelligence of Things (AIoT) and MLOps apply to IIoT and learn how to handle complex projects confidently. The book covers identifying and connecting industrial data sources from various sensors, advancing from foundational concepts to professional skills. You'll discover how to connect these sensors to cloud networks such as AWS IoT, Azure IoT, and open source IoT platforms, and extract data from the cloud to your devices. Through hands-on experience with tools such as Node-RED, OPC UA, MQTT, NoSQL, defense in depth, and Python, you'll develop streaming and batch-based AI algorithms. By the end of this book, you'll have achieved a professional level of expertise in the cloud, IoT, and AI, and be able to build more robust, efficient, and reliable IoT infrastructure for your industry.What you will learnGet a solid understanding of industrial processes, devices, and protocolsHarness IoT technology to effectively manage industrial use casesDesign and implement an IIoT network flow to continuously monitor the performance of your critical assetsGet to grips with popular cloud-based platforms such as AWS and AzureExplore Edge devices and learn about Edge and fog computing to gather field dataApply diagnostic analytics to real-world data to answer critical workforce questionsDevelop AIoT technology for predictive maintenanceWho this book is forIf you are an IoT architect, developer, AI engineer, or stakeholder involved in designing the architecture systems of the Industrial Internet of Things, this book is for you. The only prerequisite needed is a solid understanding of the Python programming language and networking concepts.

Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finishKey FeaturesGain a comprehensive understanding of LLMOps, from data handling to model governanceLeverage tools for efficient LLM lifecycle management, from development to maintenanceDiscover real-world examples of industry cutting-edge trends in generative AI operationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications. This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You'll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You'll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications. By the end of this book, you'll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.What you will learnUnderstand the evolution and impact of LLMs in AIDifferentiate between LLMOps and traditional MLOpsUtilize LLMOps tools for data analysis, preparation, and fine-tuningMaster strategies for model development, deployment, and improvementImplement techniques for model inference, serving, and scalabilityIntegrate human-in-the-loop strategies for refining LLM outputsGrasp the forefront of emerging technologies and practices in LLMOpsWho this book is forThis book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.

Elevate your AI projects with our course on Enterprise Generative AI using AWS's Well-Architected Framework, paving the way for innovation and efficiencyKey FeaturesLearn to secure AI environmentsAchieve excellence in AI architectureImplement AI with AWS solutionsBook DescriptionThe course begins with an insightful introduction to the burgeoning field of Generative AI, laying down a robust framework for understanding its applications within the AWS ecosystem. The course focuses on meticulously detailing the five pillars of the AWS Well-Architected Framework—Operational Excellence, Security, Compliance, Reliability, and Cost Optimization. Each module is crafted to provide you with a comprehensive understanding of these essential areas, integrating Generative AI technologies. You'll learn how to navigate the complexities of securing AI systems, ensuring they comply with legal and regulatory standards, and designing them for unparalleled reliability. Practical sessions on cost optimization strategies for AI projects will empower you to deliver value without compromising on performance or scalability. Furthermore, the course delves into System Architecture Excellence, emphasizing the importance of robust design principles in creating effective Generative AI solutions. The course wraps up by offering a forward-looking perspective on the Common Architectural Pattern for FM/LLM Integration & Adoption within the AWS framework. You'll gain hands-on experience with AWS solutions specifically tailored for Generative AI applications, including Lambda, API Gateway, and DynamoDB, among others.What you will learnApply Operational Excellence in AISecure Generative AI implementationsNavigate compliance in AI solutionsEnsure reliability in AI systemsOptimize costs for AI projectsIntegrate FM/LLM with AWS solutionsWho this book is forThis course is designed for IT professionals, solutions architects, and DevOps engineers looking to specialize in Generative AI. A foundational understanding of AWS and cloud computing is beneficial.

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey FeaturesGrasp the principles of data centricity and apply them to real-world scenariosGain experience with quality data collection, labeling, and synthetic data creation using PythonDevelop essential skills for building reliable, responsible, and ethical machine learning solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learnUnderstand the impact of input data quality compared to model selection and tuningRecognize the crucial role of subject-matter experts in effective model developmentImplement data cleaning, labeling, and augmentation best practicesExplore common synthetic data generation techniques and their applicationsApply synthetic data generation techniques using common Python packagesDetect and mitigate bias in a dataset using best-practice techniquesUnderstand the importance of reliability, responsibility, and ethical considerations in ML/AIWho this book is forThis book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.

Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG's role in enhancing organizational operations by blending theoretical foundations with practical techniques. You'll work with detailed coding examples using tools such as LangChain and Chroma's vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG's diverse use cases, from search engines to chatbots. You'll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you'll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what's possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents'roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.

Harness the revolutionary power of Auto-GPT and its plugins to transform your projects with advanced AI capabilitiesKey FeaturesDiscover the untapped power of Auto-GPT, opening doors to limitless AI possibilitiesCraft your own AI applications, from chat assistants to speech companions, with step-by-step guidanceExplore advanced AI topics like Docker configuration and LLM integration for cutting-edge AI developmentPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnlocking the Power of Auto-GPT and Its Plugins reveals how Auto-GPT is transforming the way we work and live, by breaking down complex goals into manageable subtasks and intelligently utilizing the internet and other tools. With a background as a self-taught full stack developer and key contributor to Auto-GPT's Inner Team, the author blends unconventional thinking with practical expertise to make Auto-GPT and its plugins accessible to developers at all levels. This book explores the potential of Auto-GPT and its associated plugins through practical applications. Beginning with an introduction to Auto-GPT, it guides you through setup, utilization, and the art of prompt generation. You'll gain a deep understanding of the various plugin types and how to create them. The book also offers expert guidance on developing AI applications such as chat assistants, research aides, and speech companions, while covering advanced topics such as Docker configuration, continuous mode operation, and integrating your own LLM with Auto-GPT. By the end of this book, you'll be equipped with the knowledge and skills needed for AI application development, plugin creation, setup procedures, and advanced Auto-GPT features to fuel your AI journey.What you will learnDevelop a solid understanding of Auto-GPT's fundamental principlesHone your skills in creating engaging and effective promptsEffectively harness the potential of Auto-GPT's versatile pluginsTailor and personalize AI applications to meet specific requirementsProficiently manage Docker configurations for advanced setupEnsure the safe and efficient use of continuous modeIntegrate your own LLM with Auto-GPT for enhanced performanceWho this book is forThis book is for developers, data scientists, and AI enthusiasts interested in leveraging the power of Auto-GPT and its plugins to create powerful AI applications. Basic programming knowledge and an understanding of artificial intelligence concepts are required to make the most of this book. Familiarity with the terminal will also be helpful.

This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to a technical audience, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.

Become 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.

Elevate your accounting skills by applying ChatGPT across audit, tax, consulting, and beyondKey FeaturesLeverage the impact of AI on modern accounting, from audits to corporate governanceUse ChatGPT to streamline your accounting tasks with practical hands-on techniquesUnderstand the impact of AI in accounting through in-depth chapters covering various domains, including ethical considerations and data analyticsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the fast-paced AI world, accounting professionals are increasingly challenged by the complexities of AI. Many struggle to integrate these advanced tools into their workflows, leading to a sense of overwhelm. ChatGPT for Accounting bridges this gap by not only simplifying AI concepts but also offering practical insights for its application in various accounting domains. This book takes you from the foundational principles of Generative Artificial Intelligence (GAI) to its practical applications in audits, tax planning, practice management, fraud examination, financial analysis, and beyond. Each chapter equips you with essential skills, showing you how AI can revolutionize internal control systems, enhance recruitment processes, streamline marketing plans, optimize tax strategies, and boost efficiency in audits. You'll then advance to exploring the role of AI in forensic accounting, financial analysis, managerial accounting, and corporate governance, while also addressing ethical and security implications. Concluding with a reflective outlook on the promises and challenges of AI, you'll gain a holistic view of the future of accounting. By the end of this book, you'll be equipped with the knowledge to harness the power of AI effectively and ethically, transforming your accounting practice and staying ahead in the ever-evolving landscape.What you will learnUnderstand the fundamentals of AI and its impact on the accounting sectorGrasp how AI streamlines and enhances the auditing process for high accuracyUncover the potential of AI in simplifying tax processes and ensuring complianceGet to grips with using AI to identify discrepancies and prevent financial fraudMaster the art of AI-powered data analytics for informed decision-makingGain insights into seamlessly integrating AI tools within existing accounting systemsStay ahead in the evolving landscape of AI-led accounting tools and practicesWho this book is forWhether you're a seasoned accounting professional, a C-suite executive, a business owner, an accounting educator, a student of accounting, or a technology enthusiast, this book provides the knowledge and insights you need to navigate the changing landscape in applying GAI technology to make a difference in all you do. An appreciation and understanding of the accounting process and concepts will be beneficial.

Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectivelyKey FeaturesUnderstand key concepts, from fundamentals through to complex topics, via a methodical approachBuild real-world end-to-end MLOps solutions and generative AI applications on Google CloudGet your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecyclePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world's leading tech companies. You'll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today's market. You'll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You'll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process. By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learnBuild solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and SparkSource, understand, and prepare data for ML workloadsBuild, train, and deploy ML models on Google CloudCreate an effective MLOps strategy and implement MLOps workloads on Google CloudDiscover common challenges in typical AI/ML projects and get solutions from expertsExplore vector databases and their importance in Generative AI applicationsUncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflowsWho this book is forThis book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.

Learn how to use Generative AI to create images, add text and color effects, and perform image editing faster than ever with step-by-step guidance from a creative community leader and educatorKey FeaturesCreate stunning visuals without complex editingEnhance your image creation skills with advanced editing techniquesDiscover practical tips to integrate Firefly with Photoshop, Illustrator, and ExpressPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionCreating conceptual art and design assets is a slow and laborious process, especially for those unfamiliar with complex design tools. With Adobe Firefly, you can say goodbye to your design challenges and start creating attractive visuals faster than ever before. Written by an Adobe expert with over two decades of experience, Extending Creativity With Adobe Firefly teaches you how to use Generative AI for art and design with easy-to-follow steps. You'll learn how to create images, add text effects, edit photos, and explore colors faster with Adobe Firefly. You'll discover Firefly's capabilities for tasks like generating text-to-image samples, styling text effects, and editing photos quickly. You'll also find how to integrate Firefly with other Adobe Creative Cloud apps to streamline your workflow. If you're just starting out, this book will acquaint you with the basics of design, enabling you to create images easily. If you are a professional, you'll learn how to improve collaboration and visual communication, and if you are a teacher, you'll be able to enhance your teaching methods with visual thinking. By the end, you'll master every function of Adobe Firefly and create outputs for website and desktop applications such as Photoshop, Illustrator, Express, and Adobe Stock. What you will learnCompare the functionality of Adobe Firefly with other similar GenAI modelsGenerate images effortlessly using text-to-image prompt engineeringEdit photos with Generative Fill, using brushes for object removal and inpaintingCreate stylish textured words and phrases through text effects and color combinations using Generative RecolorExtend your design workflow by integrating Adobe Firefly with Photoshop, Illustrator, and ExpressAccess resources for Generative AI, including example prompts and techniques for immediate useWho this book is forThis book is for students, marketers, content creators, aspiring designers, and entrepreneurs who want to craft design elements using Generative AI images and edit visuals without extensive graphic design software know-how. This book empowers you to harness the full potential of Adobe Firefly as a beginner, regardless of your design experience.

Harness the power of Midjourney to create impactful and memorable artistic outputs and gain a distinctive edge in your professional endeavorsKey FeaturesMaster Midjourney prompting with the help of practical examples from an experienced communication and web design specialistExplore Midjourney's capabilities to create visually stunning art without prior design knowledgeGain practical insights into how to strategically apply AI-generated art in your workPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionLike various other fields, AI offers boundless possibilities when it comes to art. Midjourney is one of the leading AI art creation tools that can assist you in your artistic ideas, regardless of your technical skill level. Written by an accomplished communication and web design specialist, The Midjourney Expedition is your guide to harnessing the power of AI in your creative journey. With this guide, you'll explore the extensive features of Midjourney and start creating compelling AI-generated art with ease. The first set of chapters will teach you how to set up and use Discord for personalized and seamless art creation, with a dedicated section that will help you understand the different versions of Midjourney and their capabilities. As you progress, you'll hone your prompt engineering skills, and eventually learn how to leverage the power of complex prompts. You'll also learn how Midjourney-generated images can be integrated into a multitude of workflows and domains through real-life case studies. In the last set of chapters, you'll get to grips with real-world applications of Midjourney for storytelling, creating moodboards, and more. By the end of this book, you'll not only be proficient in using Midjourney, but also understand how to strategically apply AI-generated art in your projects.What you will learnNavigate and master Midjourney's extensive features for AI art creationApply practical techniques to create visually stunning AI-generated artworkAccelerate your creative process to produce captivating visual contentUnderstand and master essential parameters to enhance your creationsCreate consistent characters for storytellingIncorporate AI-generated art into various work contextsWho this book is forThe Midjourney Expedition is for creative individuals who are looking to visually express their ideas through the power of AI. While this book will certainly benefit designers, it's equally valuable for marketing professionals, brand strategists, content creators, media managers, and entrepreneurs. Those responsible for creating compelling visual content to represent a brand, product, or concept will also find this book useful. Basic knowledge of web user interfaces will be helpful, but not required.

Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key FeaturesExamine text chunking effects on RAG workflows and understand security in RAG app developmentDiscover chatbots and agents and learn how to build complex conversation enginesBuild as you learn by applying the knowledge you gain to a hands-on projectBook DescriptionDiscover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations'. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.What you will learnUnderstand the LlamaIndex ecosystem and common use casesMaster techniques to ingest and parse data from various sources into LlamaIndexDiscover how to create optimized indexes tailored to your use casesUnderstand how to query LlamaIndex effectively and interpret responsesBuild an end-to-end interactive web application with LlamaIndex, Python, and StreamlitCustomize a LlamaIndex configuration based on your project needsPredict costs and deal with potential privacy issuesDeploy LlamaIndex applications that others can useWho this book is forThis book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.

Explore the architecture, development, and deployment strategies of large language models to unlock their full potentialKey FeaturesGain in-depth insight into LLMs, from architecture through to deploymentLearn through practical insights into real-world case studies and optimization techniquesGet a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You'll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You'll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You'll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you'll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.What you will learnExplore the architecture and components of contemporary LLMsExamine how LLMs reach decisions and navigate their decision-making processImplement and oversee LLMs effectively within your organizationMaster dataset preparation and the training process for LLMsHone your skills in fine-tuning LLMs for targeted NLP tasksFormulate strategies for the thorough testing and evaluation of LLMsDiscover the challenges associated with deploying LLMs in production environmentsDevelop effective strategies for integrating LLMs into existing systemsWho this book is forIf you're a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.

Leverage Amazon Q Developer to boost productivity and maximize efficiency by accelerating software development life cycle tasksKey FeaturesFirst book on the market to thoroughly explore all of Amazon Q Developer's featuresGain an understanding of Amazon Q Developer's capabilities across the software development life cycle through real-world examplesBuild apps with Amazon Q Developer by auto-generating code in various languages within supported IDEsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMany developers face the challenge of managing repetitive tasks and maintaining productivity. This book will help you tackle both these challenges with Amazon Q Developer, a generative AI-powered assistant designed to optimize coding and streamline workflows. This book takes you through the setup and customization of Amazon Q Developer, demonstrating how to leverage its capabilities for auto-code generation, code explanation, and transformation across multiple IDEs and programming languages. You'll learn to use Amazon Q Developer to enhance coding experiences, generate accurate code references, and ensure security by scanning for vulnerabilities. The book also shows you how to use Amazon Q Developer for AWS-related tasks, including solution building, applying architecture best practices, and troubleshooting errors. Each chapter provides practical insights and step-by-step guidance to help you fully integrate this powerful tool into your development process. You'll get to grips with effortless code implementation, explanation, transformation, and documentation, helping you create applications faster and improve your development experience. By the end of this book, you'll have mastered Amazon Q Developer to accelerate your software development lifecycle, improve code quality, and build applications faster and more efficiently.What you will learnUnderstand the importance of generative AI-powered assistants in developers'daily workEnable Amazon Q Developer for IDEs and with AWS services to leverage code suggestionsCustomize Amazon Q Developer to align with organizational coding standardsUtilize Amazon Q Developer for code explanation, transformation, and feature developmentUnderstand code references and scan for code security issues using Amazon Q DeveloperAccelerate building solutions and troubleshooting errors on AWSWho this book is forThis book is for coders, software developers, application builders, data engineers, and technical resources using AWS services looking to leverage Amazon Q Developer's features to enhance productivity and accelerate business outcomes. Basic coding skills are needed to understand the concepts covered in this book.

This book is designed for mastering prompt engineering in artificial intelligence, focusing on ChatGPT, GPT-4, and GPT plug-ins. It explores fundamental principles, practical techniques, and real-world applications. Readers will learn the role of prompts in AI interactions, the anatomy of well-constructed prompts, and various prompt styles. The book also covers setting constraints to guide AI responses and ensure ethical interactions, making it ideal for both beginners and advanced users. The journey begins with the foundations of prompts and crafting contextual prompts. It progresses to asking specific questions, providing constraints, and creating diverse content prompts. Advanced chapters cover debugging, iterating prompts, and using GPT-4 with plug-ins. The book concludes with real-world applications, future trends, and ethical considerations, ensuring a comprehensive understanding of prompt engineering. Understanding these concepts is crucial for effective AI interactions. This book transitions readers from basic to advanced prompt engineering, blending theoretical knowledge with practical skills. It is an essential resource for mastering prompt engineering and building innovative GPT applications.

Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key FeaturesDiscover Azure AI services, including computer vision, Auto ML, NLP, and OpenAIExplore AI use cases, such as image identification, chatbots, and moreWork through 145 practice questions under chapter-end self-assessments and mock examsPurchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tipsBook DescriptionThe AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources.What you will learnDiscover various types of artificial intelligence (AI)workloads and services in AzureCover Microsoft's guiding principles for responsible AI development and useUnderstand the fundamental principles of how AI and machine learning workExplore how AI models can recognize content in images and documentsGain insights into the features and use cases for natural language processingExplore the capabilities of generative AI servicesWho this book is forWhether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design in this hands-on guide, with key images printed in colorKey FeaturesMaster the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python codeGet instant access to emerging extensions and open-source modelsLeverage the power of community-shared models and LoRA to produce high-quality images that captivate audiencesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionStable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You'll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you'll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.What you will learnExplore core concepts and applications of Stable Diffusion and set up your environment for successRefine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversionHarness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and qualityExplore developments in Stable Diffusion such as video generation using AnimateDiffWrite effective prompts and leverage LLMs to automate the processDiscover how to train a Stable Diffusion LoRA from scratchWho this book is forIf you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.

Leverage LLM (large language models) for developing unmatched coding skills, solving complex problems faster, and implementing AI responsiblyKey FeaturesUnderstand the strengths and weaknesses of LLM-powered software for enhancing performance while minimizing potential issuesGrasp the ethical considerations, biases, and legal aspects of LLM-generated code for responsible AI usageBoost your coding speed and improve quality with IDE integrationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionKeeping up with the AI revolution and its application in coding can be challenging, but with guidance from AI and ML expert Dr. Vincent Hall—who holds a PhD in machine learning and has extensive experience in licensed software development—this book helps both new and experienced coders to quickly adopt best practices and stay relevant in the field. You'll learn how to use LLMs such as ChatGPT and Bard to produce efficient, explainable, and shareable code and discover techniques to maximize the potential of LLMs. The book focuses on integrated development environments (IDEs) and provides tips to avoid pitfalls, such as bias and unexplainable code, to accelerate your coding speed. You'll master advanced coding applications with LLMs, including refactoring, debugging, and optimization, while examining ethical considerations, biases, and legal implications. You'll also use cutting-edge tools for code generation, architecting, description, and testing to avoid legal hassles while advancing your career. By the end of this book, you'll be well-prepared for future innovations in AI-driven software development, with the ability to anticipate emerging LLM technologies and generate ideas that shape the future of development.What you will learnUtilize LLMs for advanced coding tasks, such as refactoring and optimizationUnderstand how IDEs and LLM tools help coding productivityMaster advanced debugging to resolve complex coding issuesIdentify and avoid common pitfalls in LLM-generated codeExplore advanced strategies for code generation, testing, and descriptionDevelop practical skills to advance your coding career with LLMsWho this book is forThis book is for experienced coders and new developers aiming to master LLMs, data scientists and machine learning engineers looking for advanced techniques for coding with LLMs, and AI enthusiasts exploring ethical and legal implications. Tech professionals will find practical insights for innovation and career growth in this book, while AI consultants and tech hobbyists will discover new methods for training and personal projects.

Gain well-rounded knowledge of AI methods in cybersecurity and obtain hands-on experience in implementing them to bring value to your organizationKey FeaturesFamiliarize yourself with AI methods and approaches and see how they fit into cybersecurityLearn how to design solutions in cybersecurity that include AI as a key featureAcquire practical AI skills using step-by-step exercises and code examplesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionArtificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You'll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that'll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you'll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.What you will learnRecognize AI as a powerful tool for intelligence analysis of cybersecurity dataExplore all the components and workflow of an AI solutionFind out how to design an AI-based solution for cybersecurityDiscover how to test various AI-based cybersecurity solutionsEvaluate your AI solution and describe its advantages to your organizationAvoid common pitfalls and difficulties when implementing AI solutionsWho this book is forThis book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you're a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.

This book is an essential guide for mastering advanced CSS3 and SVG techniques, ideal for web developers aiming to create visually striking and interactive web content. It starts with an in-depth look at CSS3, covering shadow effects, gradients, 2D and 3D transforms, and sophisticated animations. You'll learn to implement complex styling, use media queries for responsive design, and create dynamic keyframe animations. Security practices and effective use of CSS3 are also addressed.Moving to SVG, the book explores creating scalable vector graphics for advanced data visualizations and animations. You'll learn to use SVG elements like shapes, gradients, and Bezier curves, and how to seamlessly integrate them with CSS3 for dynamic, interactive graphics. Detailed tutorials on SVG filters, transforms, and animations enable you to build intricate visual effects and engaging content. Practical examples show how to combine CSS3 and SVG for responsive, high-performance web projects.By the end, you'll have the skills to craft compelling web applications using CSS3 and SVG, whether you're a beginner or an experienced developer looking to elevate your web design expertise.

This book provides a comprehensive overview of large language models (LLMs) and generative AI, covering foundational principles, advanced concepts, and practical applications. It begins with an introduction to the generative AI landscape, examining key features, popular techniques, and its influence on industries like art, business, and technology. The book also explores prominent models like ChatGPT and GPT-4, detailing their architectures, capabilities, and applications in text generation, code assistance, and conversational AI.Additionally, the book traces the evolution of LLMs, particularly focusing on the BERT family and its wide-ranging applications in natural language processing, translation, and multilingual tasks. Readers will also gain expertise in prompt engineering, learning practical tips and examples to interact more effectively with LLMs.In later chapters, the book tackles advanced topics such as fine-tuning LLMs, working with SVG, and concepts like few-shot learning and reinforcement learning. It concludes with discussions on ethical issues, biases in generative AI, and emerging trends like AI's integration with IoT, quantum computing, and neuromorphic computing. This guide equips professionals and enthusiasts with a thorough understanding of generative AI's present and future.

Expand your skillset by learning how to perform data science, machine learning, and generative AI experiments in.NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShellKey FeaturesConduct a full range of data science experiments with clear explanations from start to finishLearn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problemsAccess all of the code online as a notebook and interactive GitHub CodespacePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs the fields of data science, machine learning, and artificial intelligence rapidly evolve,.NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in.NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your.NET skills into the world of analytics and AI. With Microsoft's.NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as.NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for.NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you'll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you'll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the.NET ecosystem.What you will learnLoad, analyze, and transform data using DataFrames, data visualization, and descriptive statisticsTrain machine learning models with ML.NET for classification and regression tasksCustomize ML.NET model training pipelines with AutoML, transforms, and model trainersApply best practices for deploying models and monitoring their performanceConnect to generative AI models using Polyglot NotebooksChain together complex AI tasks with AI orchestration, RAG, and Semantic KernelCreate interactive online documentation with Mermaid charts and GitHub CodespacesWho this book is forThis book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their.NET expertise. It's ideal for those looking to learn ML.NET and Semantic kernel and extend their.NET skills to data science, machine learning, and Generative AI Workflows.

This book bridges the gap between theoretical knowledge and practical application in Python programming, machine learning, and using ChatGPT-4 in data science. It starts with an introduction to Pandas for data manipulation and analysis. The book then explores various machine learning classifiers, from kNN to SVMs. Later chapters cover GPT-4's capabilities, enhancing linear regression analysis, and using ChatGPT in data visualization, including AI apps, GANs, and DALL-E. The journey begins with mastering Pandas and machine learning fundamentals. It progresses to applying GPT-4 in linear regression and machine learning classifiers. The final chapters focus on using ChatGPT for data visualization, making complex results accessible and understandable. Understanding these concepts is crucial for modern data scientists. This book transitions readers from basic Python programming to advanced applications of ChatGPT-4 in data science. Companion files with source code, datasets, and figures enhance learning, making this an essential resource for mastering Python, machine learning, and AI-driven data visualization.