Ultimate guide to understanding and leveraging the power of large language models (LLMs). This comprehensive resource takes you through the foundational concepts, advanced techniques, and practical applications of LLMs, making it an essential read for AI enthusiasts, developers, and professionals in the field. Starting with an introduction to what LLMs are and their significance in today's technological landscape, the book delves into the history and evolution of language models, offering insights into how these models have advanced from simple statistical methods to sophisticated neural networks. The foundational concepts of Natural Language Processing (NLP) are thoroughly explained, setting the stage for a deeper understanding of LLMs. The book provides an in-depth look at key models in the field, including the GPT series, BERT, and other notable models like T5 and XLNet. Each model is explored with real-world examples, use cases, and scenarios, highlighting their unique capabilities and applications. Training LLMs is a complex process, and this book breaks it down into manageable steps, covering data collection, preprocessing, training algorithms, and the computational challenges involved. Readers will learn about the importance of fine-tuning and customizing LLMs for specific tasks and domains, ensuring optimal performance and relevance. With a focus on practical implementation, "LLM Basics" guides you through the tools and frameworks necessary for building and deploying LLM applications. The book also addresses the ethical considerations and challenges associated with LLMs, such as bias, privacy, and fairness, and provides strategies for mitigating these risks. Looking to the future, the book discusses emerging trends and technologies, the potential impact of quantum computing on LLMs, and the next frontier in language understanding. "LLM Basics" is a valuable resource for anyone looking to harness the power of large language models and stay ahead in the rapidly evolving field of AI and NLP.

Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization

Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.

Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact

Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needs Key Features Familiarize yourself with business and technical considerations involved in modeling the right database Take your data to applications, analytics, and AI with real-world examples Learn how to code, build, and deploy end-to-end solutions with expert advice Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you’ll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You’ll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learn Understand different use cases and real-world applications of data in the cloud Work with document and indexed NoSQL databases Get to grips with modeling considerations for analytics, AI, and ML Use real-world examples to learn about ETL services Design structured, semi-structured, and unstructured data for your applications and analytics Improve observability, performance, security, scalability, latency SLAs, SLIs, and SLOs Who this book is for This book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data.

Enhance your writing with AI by mastering prompt engineering techniques and become an expert in developing and utilizing LLM prompts across applications Key Features Master prompt engineering techniques to harness AI's writing potential Discover diverse LLM applications for content creation and beyond Learn through practical examples, use cases, and hands-on guidance Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Explore the different types of prompts, their strengths, and weaknesses Understand the AI agent's knowledge and mental model Enhance your creative writing with AI insights for fiction and poetry Develop advanced skills in AI chatbot creation and deployment Discover how AI will transform industries such as education, legal, and others Integrate LLMs with various tools to boost productivity Understand AI ethics and best practices, and navigate limitations effectively Experiment and optimize AI techniques for best results Who this book is for This 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.

"Building LLM Apps" is a comprehensive guide that equips readers with the knowledge and practical skills needed to develop applications utilizing large language models (LLMs). The book covers various aspects of LLM application development, starting from understanding the fundamentals of LLMs to deploying scalable and efficient solutions. Beginning with an introduction to LLMs and their importance in modern applications, the book explores the history, key concepts, and popular architectures like GPT and BERT. Readers learn how to set up their development environment, including hardware and software requirements, installing necessary tools and libraries, and leveraging cloud services for efficient development and deployment. Data preparation is essential for training LLMs, and the book provides insights into gathering and cleaning data, annotating and labeling data, and handling imbalanced data to ensure high-quality training datasets. Training large language models involves understanding training basics, best practices, distributed training techniques, and fine-tuning pre-trained models for specific tasks. Developing LLM applications requires designing user interfaces, integrating LLMs into existing systems, and building interactive features such as chatbots, text generation, sentiment analysis, named entity recognition, and machine translation. Advanced LLM techniques like prompt engineering, transfer learning, multi-task learning, and zero-shot learning are explored to enhance model capabilities. Deployment and scalability strategies are discussed to ensure smooth deployment of LLM applications while managing costs effectively. Security and ethics in LLM apps are addressed, covering bias detection, fairness, privacy, security, and ethical considerations to build responsible AI solutions. Real-world case studies illustrate the practical applications of LLMs in various domains, including customer service, healthcare, and finance. Troubleshooting and optimization techniques help readers address common issues and optimize model performance. Looking towards the future, the book highlights emerging trends and developments in LLM technology, emphasizing the importance of staying updated with advancements and adhering to ethical AI practices. "Building LLM Apps" serves as a comprehensive resource for developers, data scientists, and business professionals seeking to harness the power of large language models in their applications.

This textbook presents in accessible form information necessary for comprehensive understanding of the basics of Russian law: theory of state and law, constitutional, civil, labour, administrative and criminal law. It is based on the Russian legislation in force and the market practice, combining theoretical material, classical and modern doctrinal works, as well as the basics of the current legislation. The above will allow readers to obtain practically-oriented knowledge. This textbook is intended for lawstudents, legalscholars, legal practitioners, as well as everyone who is interested in the basics of Russian law.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Fundamentals of U.S. Law by Fernholz and Collova introduces LLM students to the common law method of case analysis through concentrated study of topics in Tort and Constitutional Law. Fundamentals of U.S. Law teaches the “how” of legal practice in the United States. Students learn how to read cases, synthesize rules from reasoning, apply those rules to novel situations, and predict how the law may develop. The authors, two experienced lawyering skills instructors, use a half dozen fascinating and controversial topics to teach the signature skill of the common-law case method. Highlights of the First Edition: LLM students are bright, motivated, legally sophisticated, and ready to succeed. Fundamentals of U.S. Law plays to their strengths and mitigates their weaknesses. The textbook starts with a very short introduction to the legal system in the United States, followed by a discussion of one example of state common-law development. The rest of the textbook presents a set of interlinked topics of American constitutional law, all of which are likely to immediately engage student interest. No boring topics allowed. Students learn how courts use their decisions to create new law, the hallmark of common-law case development. Students also learn the fundamental skills of case analysis, including rule identification, rule synthesis, and application of the rule to novel facts. Students learn to apply these skills in American-style law school examinations. Professors and students will benefit from: Lightly-edited cases in topics most likely to interest lawyers educated outside of the United States Extensive introductions before each case, placing the case in historical and legal context and indicating those issues the student should consider while reading the case Extensive editorial notes in the initial cases to help students read cases more efficiently and effectively Notes that particularly focus on developing the skills of common-law case analysis Sample exam questions at or near the end of each chapter