Large Language Models Llm And Apis

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Large Language Models - LLM and API's

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4./5 ( download)

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Book Synopsis Large Language Models - LLM and API's by : Anand Vemula

Download or read book Large Language Models - LLM and API's written by Anand Vemula and published by Anand Vemula. This book was released on with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Large Language Models API represents a transformative advancement in natural language processing (NLP), offering developers unparalleled access to state-of-the-art language models such as GPT-3. This API serves as a gateway to immense computational power and linguistic capabilities, empowering applications across diverse domains. At its core, the API provides seamless integration with existing software systems, enabling developers to harness the power of large language models without the complexities of model training and infrastructure management. By simply sending text inputs to the API, developers can receive rich, context-aware responses, opening new avenues for innovation in human-computer interaction. The API's capabilities span a wide range of tasks, including text generation, summarization, translation, sentiment analysis, and more. Whether automating content creation, enhancing customer service experiences, or powering virtual assistants, the API offers versatile solutions tailored to various use cases. Key features of the Large Language Models API include robust performance, scalability, and reliability. With access to vast amounts of training data and sophisticated neural network architectures, the API consistently delivers high-quality results across different languages and domains. Additionally, its scalable infrastructure ensures smooth operation even under heavy workloads, making it suitable for applications of any scale. Ethical considerations are paramount in AI development, and the API prioritizes responsible usage through features such as content moderation and bias detection. Developers can leverage these tools to mitigate the risks of misinformation, bias, and privacy violations, fostering trust and integrity in their applications. The API's documentation and developer resources provide comprehensive guidance for integration and usage, catering to developers of all skill levels. Additionally, community support and online forums offer opportunities for collaboration and knowledge sharing, driving innovation and collective learning. As the field of NLP continues to evolve, the Large Language Models API remains at the forefront of innovation, with ongoing updates and improvements to meet the evolving needs of developers and users alike. By leveraging the API's capabilities responsibly and creatively, developers can unlock new possibilities and redefine the boundaries of human-computer interaction.

Mastering Large Language Models with Python

Author : Raj Arun R
Publisher : Orange Education Pvt Ltd
ISBN 13 : 8197081824
Total Pages : 547 pages
Book Rating : 4.1/5 (97 download)

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Book Synopsis Mastering Large Language Models with Python by : Raj Arun R

Download or read book Mastering Large Language Models with Python written by Raj Arun R and published by Orange Education Pvt Ltd. This book was released on 2024-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Programming Large Language Models with Azure Open AI

Author : Francesco Esposito
Publisher : Microsoft Press
ISBN 13 : 0138280452
Total Pages : 605 pages
Book Rating : 4.1/5 (382 download)

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Book Synopsis Programming Large Language Models with Azure Open AI by : Francesco Esposito

Download or read book Programming Large Language Models with Azure Open AI written by Francesco Esposito and published by Microsoft Press. This book was released on 2024-04-03 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software

Building LLM Applications with Python: A Practical Guide

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4./5 ( download)

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Book Synopsis Building LLM Applications with Python: A Practical Guide by : Anand Vemula

Download or read book Building LLM Applications with Python: A Practical Guide written by Anand Vemula and published by Anand Vemula. This book was released on with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book equips you to harness the remarkable capabilities of Large Language Models (LLMs) using Python. Part I unveils the world of LLMs. You'll delve into their inner workings, explore different LLM types, and discover their exciting applications in various fields. Part II dives into the practical side of things. We'll guide you through setting up your Python environment and interacting with LLMs. Learn to craft effective prompts to get the most out of LLMs and understand the different response formats they can generate. Part III gets you building! We'll explore how to leverage LLMs for creative text generation, from poems and scripts to code snippets. Craft effective question-answering systems and build engaging chatbots – the possibilities are endless! Part IV empowers you to maintain and improve your LLM creations. We'll delve into debugging techniques to identify and resolve issues. Learn to track performance and implement optimizations to ensure your LLM applications run smoothly. This book doesn't shy away from the bigger picture. The final chapter explores the ethical considerations of LLMs, addressing bias and promoting responsible use of this powerful technology. By the end of this journey, you'll be equipped to unlock the potential of LLMs with Python and contribute to a future brimming with exciting possibilities.

Generative AI with LangChain

Author : Ben Auffarth
Publisher : Packt Publishing Ltd
ISBN 13 : 1835088368
Total Pages : 369 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Generative AI with LangChain by : Ben Auffarth

Download or read book Generative AI with LangChain written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2023-12-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Training Your Own Large Language Model

Author : StoryBuddiesPlay
Publisher : StoryBuddiesPlay
ISBN 13 :
Total Pages : 65 pages
Book Rating : 4./5 ( download)

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Book Synopsis Training Your Own Large Language Model by : StoryBuddiesPlay

Download or read book Training Your Own Large Language Model written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-04-26 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the Power of Language with Large Language Models: Your Comprehensive Guide The ability to understand and generate human language is a cornerstone of human intelligence. Artificial intelligence (AI) is rapidly evolving, and Large Language Models (LLMs) are at the forefront of this revolution. These powerful AI tools can process and generate text with remarkable fluency, making them ideal for various applications. This comprehensive guide empowers you to step into the exciting world of LLMs and train your own! Whether you're a seasoned developer, an AI enthusiast, or simply curious about the future of language technology, this book equips you with the knowledge and tools to navigate the LLM landscape. Within these pages, you'll discover: The transformative potential of LLMs: Explore the various tasks LLMs can perform, from generating creative text formats to answering your questions in an informative way, and even translating languages. A step-by-step approach to LLM training: Learn how to define your project goals, identify the right data sources, and choose the optimal LLM architecture for your needs. Essential tools and techniques: Gain insights into popular frameworks like TensorFlow and PyTorch, and delve into practical aspects like data pre-processing and hyperparameter tuning. Fine-tuning and deployment strategies: Unleash the full potential of your LLM by tailoring it to specific tasks and seamlessly integrating it into your applications or workflows. The future of LLMs: Explore cutting-edge advancements like explainable AI and lifelong learning, and discover the potential impact of LLMs on various aspects of society. By the time you finish this guide, you'll be equipped to: Confidently define and plan your LLM project. Train your own LLM using powerful AI frameworks and techniques. Fine-tune your LLM for real-world applications. Deploy and integrate your LLM for seamless functionality. Contribute to the ever-evolving field of large language models. Don't wait any longer! Dive into the world of LLMs and unlock the power of language manipulation with this comprehensive guide. Get started on your LLM journey today!

Large Language Models Projects

Author : Pere Martra Manonelles
Publisher : Apress
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (688 download)

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Book Synopsis Large Language Models Projects by : Pere Martra Manonelles

Download or read book Large Language Models Projects written by Pere Martra Manonelles and published by Apress. This book was released on 2024-10-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks

Building LLM Powered Applications

Author : Valentina Alto
Publisher : Packt Publishing Ltd
ISBN 13 : 1835462634
Total Pages : 343 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Building LLM Powered Applications by : Valentina Alto

Download or read book Building LLM Powered Applications written by Valentina Alto and published by Packt Publishing Ltd. This book was released on 2024-05-22 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

Building Large Language Model(LLM) Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 77 pages
Book Rating : 4./5 ( download)

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Book Synopsis Building Large Language Model(LLM) Applications by : Anand Vemula

Download or read book Building Large Language Model(LLM) Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

The Developer's Playbook for Large Language Model Security

Author : Steve Wilson
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098162161
Total Pages : 197 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis The Developer's Playbook for Large Language Model Security by : Steve Wilson

Download or read book The Developer's Playbook for Large Language Model Security written by Steve Wilson and published by "O'Reilly Media, Inc.". This book was released on 2024-09-03 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Demystifying Large Language Models with Examples

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 40 pages
Book Rating : 4./5 ( download)

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Book Synopsis Demystifying Large Language Models with Examples by : Anand Vemula

Download or read book Demystifying Large Language Models with Examples written by Anand Vemula and published by Anand Vemula. This book was released on with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying large language models (LLMs), this book explores their inner workings, showcases their applications, and ponders their future impact. Part 1: Unveiling the LLM Landscape unveils the secrets behind these AI marvels. You'll learn how LLMs, trained on massive datasets of text and code, can understand and generate human-like language. Different LLM architectures and the key players developing them are also explored, providing a solid foundation for understanding this rapidly evolving field. Part 2: LLMs in Action brings these models to life with a showcase of their capabilities. From creating poems and code to summarizing complex information and translating languages, LLMs are transforming how we interact with machines. The book delves into how LLMs power chatbots and virtual assistants, automate repetitive coding tasks, and even assist programmers with debugging. Part 3: The Future of LLMs tackles the challenges and ethical considerations surrounding LLMs. It emphasizes the importance of mitigating bias in their outputs and ensuring transparency in their decision-making. Security and privacy concerns are also addressed, highlighting the need for responsible development practices. Looking ahead, the book explores how LLMs will revolutionize various industries. Education, customer service, and marketing are just a few examples where LLMs hold the potential to personalize experiences and streamline processes. The impact on creative fields is also discussed, with LLMs potentially serving as tools for inspiration while human creativity remains paramount. The book concludes by emphasizing the potential of LLMs and the importance of responsible development. By understanding their capabilities and limitations, we can harness the power of LLMs to shape a better future. This future hinges on ensuring LLMs are unbiased, transparent, and used for positive societal impact.

Generative AI and LLMs

Author : S. Balasubramaniam
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 311142507X
Total Pages : 290 pages
Book Rating : 4.1/5 (114 download)

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Book Synopsis Generative AI and LLMs by : S. Balasubramaniam

Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

Large Language Models

Author : Oswald Campesato
Publisher : Stylus Publishing, LLC
ISBN 13 : 1501520601
Total Pages : 517 pages
Book Rating : 4.5/5 (15 download)

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Book Synopsis Large Language Models by : Oswald Campesato

Download or read book Large Language Models written by Oswald Campesato and published by Stylus Publishing, LLC. This book was released on 2024-09-17 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, Meta AI, Claude 3, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. FEATURES: Covers in-depth explanations of foundational and advanced LLM concepts, including BERT, GPT-4, and prompt engineering Uses practical Python code samples in leveraging LLM functionalities effectively Discusses future trends, ethical considerations, and the evolving landscape of AI technologies Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase)

The Developer's Playbook for Large Language Model Security

Author : Steve Wilson
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109816217X
Total Pages : 200 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis The Developer's Playbook for Large Language Model Security by : Steve Wilson

Download or read book The Developer's Playbook for Large Language Model Security written by Steve Wilson and published by "O'Reilly Media, Inc.". This book was released on 2024-09-03 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

The LLM Security Handbook: Building Trustworthy AI Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4./5 ( download)

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Book Synopsis The LLM Security Handbook: Building Trustworthy AI Applications by : Anand Vemula

Download or read book The LLM Security Handbook: Building Trustworthy AI Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a world increasingly powered by artificial intelligence, Large Language Models (LLMs) are emerging as powerful tools capable of generating human-quality text, translating languages, and writing different creative content. However, this power comes with hidden risks. This book dives deep into the world of LLM security, providing a comprehensive guide for developers, security professionals, and anyone interested in harnessing the potential of LLMs responsibly. Part 1: Understanding the Landscape The book starts by unpacking the inner workings of LLMs and explores how these models can be misused to generate harmful content or leak sensitive data. We delve into the concept of LLM bias, highlighting how the data used to train these models can influence their outputs. Through real-world scenarios and case studies, the book emphasizes the importance of proactive security measures to mitigate these risks. Part 2: Building Secure LLM Applications The core of the book focuses on securing LLM applications throughout their development lifecycle. We explore the Secure Development Lifecycle (SDLC) for LLMs, emphasizing secure data acquisition, robust model testing techniques, and continuous monitoring strategies. The book delves into MLOps security practices, highlighting techniques for securing model repositories, implementing anomaly detection, and ensuring the trustworthiness of LLM models. Part 3: Governance and the Future of LLM Security With the rise of LLMs, legal and ethical considerations come to the forefront. The book explores data privacy regulations and how to ensure responsible AI development practices. We discuss the importance of explainability and transparency in LLM decision-making for building trust and addressing potential biases. Looking ahead, the book explores emerging security threats and emphasizes the importance of continuous improvement and collaboration within the LLM security community. By proactively addressing these challenges, we can ensure a secure future for LLM applications.

Machine Learning with PyTorch and Scikit-Learn

Author : Sebastian Raschka
Publisher : Packt Publishing Ltd
ISBN 13 : 1801816387
Total Pages : 775 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka

Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

LLM Prompt Engineering for Developers

Author : Aymen El Amri
Publisher : Packt Publishing Ltd
ISBN 13 : 1836201729
Total Pages : 251 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis LLM Prompt Engineering for Developers by : Aymen El Amri

Download or read book LLM Prompt Engineering for Developers written by Aymen El Amri and published by Packt Publishing Ltd. This book was released on 2024-05-23 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the dynamic field of LLM prompt engineering with this book. Starting with fundamental NLP principles & progressing to sophisticated prompt engineering methods, this book serves as the perfect comprehensive guide. Key Features In-depth coverage of prompt engineering from basics to advanced techniques. Insights into cutting-edge methods like AutoCoT and transfer learning. Comprehensive resource sections including prompt databases and tools. Book Description"LLM Prompt Engineering For Developers" begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering. As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology. This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.What you will learn Understand the principles of NLP and their application in LLMs. Set up and configure environments for developing with LLMs. Implement few-shot and zero-shot learning techniques. Enhance LLM outputs through AutoCoT and self-consistency methods. Apply transfer learning to adapt LLMs to new domains. Develop practical skills in testing & scoring prompt effectiveness. Who this book is for The target audience for "LLM Prompt Engineering For Developers" includes software developers, AI enthusiasts, technical team leads, advanced computer science students, and AI researchers with a basic understanding of artificial intelligence. Ideal for those looking to deepen their expertise in large language models and prompt engineering, this book serves as a practical guide for integrating advanced AI-driven projects and research into various workflows, assuming some foundational programming knowledge and familiarity with AI concepts.