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Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 71
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Book Description
Fine-Tuning LLMs: A Developer’s Guide to Custom AI Models" serves as a comprehensive resource for developers looking to adapt pre-trained large language models (LLMs) for specific applications. The book begins by tracing the historical evolution of LLMs, detailing their transition from traditional natural language processing (NLP) models to the sophisticated architectures used today. It emphasizes the importance of fine-tuning, which involves training a pre-existing model on a smaller, domain-specific dataset to enhance its performance on targeted tasks.The guide outlines various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches. Each method is discussed in detail, highlighting its implications for different applications. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering essential aspects such as data preparation, model initialization, training setup, and deployment strategies.Key considerations for successful fine-tuning are explored, including hyperparameter tuning, handling imbalanced datasets, and employing parameter-efficient techniques like Low-Rank Adaptation (LoRA). The book also addresses evaluation and validation processes, emphasizing the importance of monitoring performance metrics and ensuring model safety.Furthermore, the guide discusses advanced techniques such as memory tuning and mixture of experts, which enhance model efficiency and adaptability. By integrating theoretical insights with practical applications, this book equips developers with the knowledge and tools necessary to leverage LLMs effectively in various domains.Overall, "Fine-Tuning LLMs" is an essential reference for anyone interested in harnessing the power of large language models to create custom AI solutions that meet specific needs.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 71
Get Book Here
Book Description
Fine-Tuning LLMs: A Developer’s Guide to Custom AI Models" serves as a comprehensive resource for developers looking to adapt pre-trained large language models (LLMs) for specific applications. The book begins by tracing the historical evolution of LLMs, detailing their transition from traditional natural language processing (NLP) models to the sophisticated architectures used today. It emphasizes the importance of fine-tuning, which involves training a pre-existing model on a smaller, domain-specific dataset to enhance its performance on targeted tasks.The guide outlines various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches. Each method is discussed in detail, highlighting its implications for different applications. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering essential aspects such as data preparation, model initialization, training setup, and deployment strategies.Key considerations for successful fine-tuning are explored, including hyperparameter tuning, handling imbalanced datasets, and employing parameter-efficient techniques like Low-Rank Adaptation (LoRA). The book also addresses evaluation and validation processes, emphasizing the importance of monitoring performance metrics and ensuring model safety.Furthermore, the guide discusses advanced techniques such as memory tuning and mixture of experts, which enhance model efficiency and adaptability. By integrating theoretical insights with practical applications, this book equips developers with the knowledge and tools necessary to leverage LLMs effectively in various domains.Overall, "Fine-Tuning LLMs" is an essential reference for anyone interested in harnessing the power of large language models to create custom AI solutions that meet specific needs.
Author: Sinan Ozdemir
Publisher: Addison-Wesley Professional
ISBN: 013534655X
Category : Computers
Languages : en
Pages : 584
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Book Description
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Arup Das
Publisher: TinyTechMedia LLC
ISBN:
Category : Computers
Languages : en
Pages : 103
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Book Description
Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of? This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI. In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries. Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!™
Author: Lior Gazit
Publisher: Packt Publishing Ltd
ISBN: 1804616389
Category : Computers
Languages : en
Pages : 340
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Book Description
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 Features Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python Model and classify text using traditional machine learning and deep learning methods Understand the theory and design of LLMs and their implementation for various applications in AI Explore NLP insights, trends, and expert opinions on its future direction and potential Who this book is for This 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.
Author: Flavia Monti
Publisher: Springer Nature
ISBN: 981970989X
Category :
Languages : en
Pages : 364
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Book Description
Author: Ronald Legarski
Publisher: SolveForce
ISBN:
Category : Computers
Languages : en
Pages : 746
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Book Description
"LLMs: From Origin to Present and Future Applications" by Ronald Legarski is an authoritative exploration of Large Language Models (LLMs) and their profound impact on artificial intelligence, machine learning, and various industries. This comprehensive guide traces the evolution of LLMs from their early beginnings to their current applications, and looks ahead to their future potential across diverse fields. Drawing on extensive research and industry expertise, Ronald Legarski provides readers with a detailed understanding of how LLMs have developed, the technologies that power them, and the transformative possibilities they offer. This book is an invaluable resource for AI professionals, researchers, and enthusiasts who want to grasp the intricacies of LLMs and their applications in the modern world. Key topics include: The Origins of LLMs: A historical perspective on the development of natural language processing and the key milestones that led to the creation of LLMs. Technological Foundations: An in-depth look at the architecture, data processing, and training techniques that underpin LLMs, including transformer models, tokenization, and attention mechanisms. Current Applications: Exploration of how LLMs are being used today in industries such as healthcare, legal services, education, content creation, and more. Ethical Considerations: A discussion on the ethical challenges and societal impacts of deploying LLMs, including bias, fairness, and the need for responsible AI governance. Future Directions: Insights into the future of LLMs, including their role in emerging technologies, interdisciplinary research, and the potential for creating more advanced AI systems. With clear explanations, practical examples, and forward-thinking perspectives, "LLMs: From Origin to Present and Future Applications" equips readers with the knowledge to navigate the rapidly evolving field of AI. Whether you are a seasoned AI professional, a researcher in the field, or someone with an interest in the future of technology, this book offers a thorough exploration of LLMs and their significance in the digital age. Discover how LLMs are reshaping industries, driving innovation, and what the future holds for these powerful AI models.
Author: John Sotiropoulos
Publisher: Packt Publishing Ltd
ISBN: 1835088678
Category : Computers
Languages : en
Pages : 586
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Book Description
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 77
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Book Description
Adaptive AI" delves into the transformative capabilities of large language models (LLMs) and the critical techniques of fine-tuning and few-shot learning that enhance their adaptability across various applications. The book provides a comprehensive overview of LLMs, tracing their evolution from early models like GPT-1 to the sophisticated architectures of GPT-4 and beyond. The introduction emphasizes the significance of customization in deploying LLMs effectively, highlighting how organizations can leverage these models to meet specific needs, improve user experiences, and drive innovation. The book then defines key concepts, explaining the distinction between fine-tuning—where a pre-trained model is further trained on a domain-specific dataset—and few-shot learning, which enables models to generalize from minimal examples. Throughout the chapters, "Adaptive AI" presents practical insights into implementing fine-tuning and few-shot learning. It covers use cases across diverse sectors, including healthcare, finance, and customer support, illustrating how fine-tuning can enhance language models' understanding of specialized vocabulary and context. Conversely, the few-shot learning section showcases its utility in scenarios with limited data, demonstrating how LLMs can perform effectively even when trained on just a few examples. The book also explores hybrid approaches, combining both fine-tuning and few-shot learning to maximize model performance. It discusses methodologies for evaluating model effectiveness and addresses the ethical considerations and challenges associated with deploying these technologies. In conclusion, "Adaptive AI" serves as a vital resource for AI practitioners, researchers, and industry professionals seeking to harness the full potential of large language models. By providing actionable strategies and real-world case studies, the book equips readers with the knowledge to effectively customize LLMs for diverse applications, paving the way for innovation in AI-driven solutions.
Author: Rachelle Palmer
Publisher: Packt Publishing Ltd
ISBN: 1836207247
Category : Computers
Languages : en
Pages : 299
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Book Description
Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
Author: Etienne Noumen
Publisher: Etienne Noumen
ISBN:
Category : Computers
Languages : en
Pages : 249
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Book Description
Dive into the revolutionary world of Artificial Intelligence with 'AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence'. This comprehensive guide is your portal to understanding AI's most intricate concepts and cutting-edge developments. Whether you're a curious beginner or an AI enthusiast, this book is tailored to unveil the complexities of AI in a simple, accessible manner. What's Inside: Fundamental AI Concepts: Journey through the basics of AI, machine learning, deep learning, and neural networks. AI in Action: Explore how AI is reshaping industries and society, diving into its applications in computer vision, natural language processing, and beyond. Ethical AI: Tackle critical issues like AI ethics and bias, understanding the moral implications of AI advancements. Industry Insights: Gain insights into how AI is revolutionizing industries and impacting our daily lives. The Future of AI: Forecast the exciting possibilities and challenges that lie ahead in the AI landscape. Special Focus on Generative AI & LLMs: Latest AI Trends: Stay updated with the latest in AI, including ChatGPT, Google Gemini, GPT-x, Gemini, and more. Interactive Quizzes: Test your knowledge with engaging quizzes on Generative AI and Large Language Models (LLMs). Practical Guides: Master GPT-x with a simplified guide, delve into advanced prompt engineering, and explore the nuances of temperature settings in AI. Real-World Applications: Learn how to leverage AI in various sectors, from healthcare to cybersecurity, and even explore its potential in areas like aging research and brain implants. For the AI Enthusiast: Prompt Engineering: Uncover secrets to crafting effective prompts for ChatGPT/Google Gemini. AI Career Insights: Explore lucrative career paths in AI, including roles like AI Prompt Engineers. AI Investment Guide: Navigate the world of AI stocks and investment opportunities. For AI Developers: How to develop AI-powered apps effectively? Generative AI Technology Stack Overview – A Comprehensive Guide Your Guide to Navigating AI: Do-It-Yourself Tutorials: From building custom ChatGPT applications to running LLMs locally, this book offers step-by-step guides. AI for Everyday Use: Learn how AI can assist in weight loss, social media, and more. 'AI Unraveled' is more than just a book; it's a resource for anyone looking to grasp the complexities of AI and its impact on our world. Get ready to embark on an enlightening journey into the realm of Artificial Intelligence!" More Topics Covered: Artificial Intelligence, Machine Learning, Deep Learning, NLP, AI Ethics, Robotics, Cognitive Computing, ChatGPT, OpenAI, Google Gemini, Generative AI, LLMs, AI in Healthcare, AI Investments, and much more. GPT-x vs Gemini: Pros and Cons Mastering GPT-x: Simplified Guide For everyday Users Advance Prompt Engineering Techniques: [Single Prompt Technique, Zero-Shot and Few-Shot, Zero-Shot and Few-Shot, Generated Knowledge Prompting, EmotionPrompt, Chain of Density (CoD), Chain of Thought (CoT), Validation of LLMs Responses, Chain of Verification (CoVe), Agents - The Frontier of Prompt Engineering, Prompt Chaining vs Agents, Tree of Thought (ToT), ReAct (Reasoning + Act), ReWOO (Reasoning WithOut Observation), Reflexion and Self-Reflection, Guardrails, RAIL (Reliable AI Markup Language), Guardrails AI, NeMo Guardrails] Understanding Temperature in GPT-x: A Guide to AI Probability and Creativity Retrieval-Augmented Generation (RAG) model in the context of Large Language Models (LLMs) like GPT-x Prompt Ideas for ChatGPT/Google Gemini How to Run ChatGPT-like LLMs Locally on Your Computer in 3 Easy Steps ChatGPT Custom Instructions Settings for Power Users Examples of bad and good ChatGPT prompts Top 5 Beginner Mistakes in Prompt Engineering Use ChatGPT like a PRO Prompt template for learning any skill Prompt Engineering for ChatGPT The Future of LLMs in Search What is Explainable AI? Which industries are meant for XAI? ChatGPT Best Tips, Cheat Sheet LLMs Utilize Vector DB for Data Storage The Limitation Technique in Prompt Responses Use ChatGPT to learn new subjects Prompts to proofread anything How to Create a Specialized LLM That Understands Your Custom Data Topics: Artificial Intelligence Education Machine Learning Deep Learning Reinforcement Learning Neural networks Data science AI ethics Deepmind Robotics Natural language processing Intelligent agents Cognitive computing AI Apps AI impact AI Tech ChatGPT Open AI Safe AI Generative AI Discriminative AI Sam Altman Google Gemini NVDIA Large Language Models (LLMs) PALM GPT Explainable AI GPUs AI Stocks AI Podcast Q* AI Certification AI Quiz RAG Context Windows Tokens Ai Agents How to access the AI Unraveled: Djamgatech: https://djamgatech.com/product/ai-unraveled-demystifying-frequently-asked-questions-on-artificial-intelligence-paperback-print-book Google eBook: https://play.google.com/store/books/details?id=oySuEAAAQBAJ Apple eBook: https://books.apple.com/us/book/id6445730691 Etsy: https://www.etsy.com/ca/listing/1617575707/ai-unraveled-demystifying-frequently Audible at Amazon : https://www.audible.com/pd/B0BXMJ7FK5/?source_code=AUDFPWS0223189MWT-BK-ACX0-343437&ref=acx_bty_BK_ACX0_343437_rh_us (Use Promo code: 37YT3B5UYUYZW) Audiobook at Google: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM
