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Paperback. Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : new. Paperback. In the rapidly evolving landscape of artificial intelligence and data management, "Harnessing Snowflake with Generative AI and LLMs" serves as a comprehensive guide for leveraging the powerful combination of Snowflake's Data Cloud and advanced generative AI technologies, particularly Large Language Models (LLMs). This book is designed for data scientists, AI practitioners, and business leaders eager to unlock the full potential of their data and AI initiatives.The book begins with an introduction to the foundational concepts of generative AI and LLMs, explaining their significance and impact on various industries. It provides a clear overview of Snowflake's unique architecture and capabilities, highlighting its ability to efficiently store, manage, and analyze large volumes of data. By integrating Snowflake with generative AI, readers will learn how to build scalable and efficient AI-driven solutions that can handle complex data processing tasks in real-time.A detailed exploration of integrating generative AI with Snowflake follows, showcasing practical applications and real-world use cases. These include enhancing customer support through advanced chatbots, automating content creation for marketing, and implementing predictive analytics for more informed business decisions. The book emphasizes best practices for using Snowflake with LLMs, covering data quality, scalability, and security considerations.Further chapters delve into the technical aspects of Snowflake, such as data warehousing, real-time data processing, and performance optimization for AI workloads. The book also addresses crucial ethical considerations, including bias, fairness, and data privacy, ensuring that readers are equipped to develop responsible and compliant AI solutions.Looking ahead, the book explores emerging technologies and future trends in generative AI and Snowflake, providing insights into the next wave of innovations. Readers will gain a thorough understanding of how to stay ahead in the AI landscape by continuously adapting to new advancements."Harnessing Snowflake with Generative AI and LLMs" is an essential resource for anyone looking to effectively combine cutting-edge AI techniques with robust data management practices, driving transformative results in their organization. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Paperback. Etat : new. Paperback. "Large Language Models: A Step-by-Step Do It Yourself Guide" is an essential resource for those looking to understand and develop large language models (LLMs) from scratch. This comprehensive guide takes readers through the entire process, from foundational concepts to advanced techniques, ensuring a thorough understanding of both the theory and practical application of LLMs.The book begins with an introduction to LLMs, covering their definitions, historical evolution, and key concepts. It explores various applications, including natural language processing, conversational AI, and text generation. Ethical considerations, such as bias and privacy, are also addressed, setting the stage for responsible AI development.In the next section, readers are guided through the process of building their own LLMs. This includes setting up the development environment, understanding essential machine learning concepts, and collecting and preparing data. Detailed tutorials on model architecture and design follow, including insights into transformers, attention mechanisms, and custom model design. Training strategies and techniques are discussed, with practical examples of fine-tuning and transfer learning.The book then shifts focus to deployment and practical use. It covers various deployment strategies, integrating LLMs with applications and services, and best practices for monitoring and maintaining models. Hands-on projects such as creating chatbots, text summarization tools, and personalized recommendation systems are included, offering readers real-world experience.Advanced topics, including innovative training methods and case studies, round out the guide. Real-world examples, like implementing customer support bots and automating content generation, provide valuable insights into practical applications of LLMs.Overall, this guide equips readers with the knowledge and skills needed to build, deploy, and optimize their own large language models, making it an indispensable resource for AI enthusiasts and professionals alike. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Etat : New.

Paperback. Etat : new. Paperback. Large Language Models Essentials: Techniques, Tools, and Applications" provides a comprehensive introduction to the foundational and advanced aspects of large language models (LLMs). This book is designed for professionals, researchers, and enthusiasts who want to understand the core techniques, tools, and practical applications of LLMs in modern natural language processing (NLP).The book begins with a clear overview of LLMs, explaining their significance and evolution from early language models to today's sophisticated systems. It covers essential concepts, including the underlying architectures and terminology, providing a solid grounding for readers.In the "Techniques" section, readers will explore the various methods used to train and fine-tune LLMs. The book delves into data collection, preprocessing, and optimization techniques, offering practical insights into improving model performance. Detailed examples and code snippets illustrate how these techniques are applied in real-world scenarios.The "Tools" chapter introduces popular frameworks and platforms for working with LLMs, such as TensorFlow and PyTorch. It guides readers through the setup and configuration of these tools, ensuring they are well-equipped to implement and experiment with LLMs.The "Applications" section demonstrates how LLMs are used across different domains, from creative writing and code generation to machine translation and conversational agents. Case studies highlight successful implementations and provide actionable insights into how LLMs can address specific industry needs.Overall, this book equips readers with the knowledge and skills necessary to leverage LLMs effectively, combining theoretical understanding with practical, hands-on guidance. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Paperback. Etat : new. Paperback. In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as powerful tools transforming numerous industries. "Designing Large Language Model Systems: System Design, Architecture, Deployment, and Operationalization" provides a comprehensive guide to understanding, building, and deploying these complex models effectively. This book is an essential resource for AI practitioners, developers, and researchers seeking to harness the full potential of LLMs.Starting with an in-depth introduction to LLMs, the book covers their fundamental principles, including neural networks, transformers, and the key architectures like GPT, BERT, and T5. It delves into the historical evolution and the diverse applications of LLMs, providing context and grounding for readers.The core of the book is divided into several parts, each focusing on critical aspects of LLM system design and implementation. Readers will explore system architecture, learning about the essential components, hardware requirements, and the best software frameworks. The book provides detailed guidance on designing efficient data pipelines, ensuring data quality, and optimizing model training infrastructure.Deployment strategies are covered extensively, with insights into on-premise, cloud, and hybrid deployment models. The book offers practical advice on serving LLMs at scale, optimizing latency and throughput, and ensuring security and compliance. Operationalization is another key focus, with chapters on monitoring, maintenance, performance metrics, and cost management.Advanced topics include scalability, performance optimization, and integration with other systems, offering case studies of successful implementations. The book also addresses ethical and social considerations, emphasizing the importance of fairness, transparency, and accountability in AI design.To provide practical experience, the book includes hands-on projects such as building a chatbot, developing a text generation system, and creating an AI-powered recommendation engine. Each project is accompanied by step-by-step tutorials and complete solutions, enabling readers to apply their knowledge effectively."Designing Large Language Model Systems" is not just a technical guide; it is a comprehensive resource that bridges theory and practice, equipping readers with the skills and knowledge to build, deploy, and manage large language models in real-world applications Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Paperback. Etat : new. Paperback. Mastering Fine-Tuning with LLMs: From Basics to Advanced Techniques" provides a comprehensive guide to optimizing large language models (LLMs) for various applications. This book is designed for data scientists, machine learning engineers, and AI enthusiasts who want to harness the power of fine-tuning to enhance LLMs' performance and adapt them to specific tasks.The book is structured into eight parts, each focusing on different aspects of fine-tuning LLMs. It begins with an introduction to large language models, including their evolution, key concepts, and applications such as natural language understanding, text generation, and conversational AI.Part II delves into the fundamentals of fine-tuning, covering essential topics like data preparation, setting up the fine-tuning environment, and understanding the differences between pre-training and fine-tuning.In Part III, the book explores various fine-tuning techniques, including supervised, unsupervised, and self-supervised learning, along with transfer learning and domain adaptation. It provides step-by-step guides and case studies to illustrate how these techniques can be applied in real-world scenarios.Part IV focuses on advanced fine-tuning strategies, such as hyperparameter tuning, handling imbalanced and limited data, and optimizing training performance. It offers practical advice on managing computational resources and improving efficiency.Part V addresses evaluating and validating fine-tuned models, discussing evaluation metrics, validation techniques, and error analysis to ensure model reliability and performance.Part VI covers deployment and maintenance, including strategies for deploying models in production, monitoring performance, and updating models to adapt to new data.In Part VII, the book delves into ethical considerations and cost management, emphasizing the importance of fairness, transparency, and cost-effective practices in AI development.Finally, Part VIII presents hands-on projects and case studies, allowing readers to apply their knowledge in practical scenarios. The book concludes with a look at future trends in LLM fine-tuning and the evolving role of LLMs in AI. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : new. Paperback. Generative AI Hands-On: A Practical Guide to Model Development and Real-World Applications is a hands-on resource designed to equip readers with the skills and knowledge needed to build and apply generative AI models effectively. This comprehensive guide covers the entire process, from foundational concepts to advanced techniques, offering practical insights into real-world applications.The book begins with an introduction to generative AI, explaining core concepts and its evolution. It outlines how generative models differ from other AI approaches and explores their diverse applications in fields such as text generation, image synthesis, and audio creation. Readers will gain a solid understanding of how these models work and their potential uses.Part II focuses on the technical foundation of generative models, including machine learning basics, neural networks, and deep learning techniques. It delves into key types of generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models, providing a clear explanation of their functionalities and differences.The practical aspect of the book is emphasized in Part III, which guides readers through setting up their environment, including software, tools, and libraries. It covers data preparation, model implementation, and fine-tuning, allowing readers to create their own generative models.Part IV showcases practical applications, offering hands-on projects for text, image, and audio generation. Case studies highlight the impact of generative AI in various domains, demonstrating its versatility and creativity.The final part addresses ethical considerations and future trends in generative AI, covering topics such as bias, fairness, and emerging technologies. This ensures readers are prepared to navigate the evolving landscape responsibly."Generative AI Hands-On" is an essential guide for anyone looking to harness the power of generative AI for practical and innovative applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Etat : New.