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Decoding Large Language Models: An exhaustive guide to understanding, implementing, and optimizing LLMs for NLP applications
Explore the architecture, development, and deployment strategies of large language models to unlock their full potential
Key FeaturesGain in-depth insight into LLMs, from architecture through to deploymentLearn through practical insights into real-world case studies and optimization techniquesGet a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications.
You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP.
By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.
What you will learnExplore the architecture and components of contemporary LLMsExamine how LLMs reach decisions and navigate their decision-making processImplement and oversee LLMs effectively within your organizationMaster dataset preparation and the training process for LLMsHone your skills in fine-tuning LLMs for targeted NLP tasksFormulate strategies for the thorough testing and evaluation of LLMsDiscover the challenges associated with deploying LLMs in production environmentsDevelop effective strategies for integrating LLMs into existing systemsWho this book is forIf you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.
Table of ContentsLLM ArchitectureHow LLMs Make DecisionsThe Mechanics of Training LLMsAdvanced Training StrategiesFine-Tuning LLMs for Specific ApplicationsTesting and Evaluating LLMsDeploying LLMs in ProductionStrategies for Integrating LLMsOptimization Techniques for PerformanceAdvanced Optimization and EfficiencyLLM Vulnerabilities, Biases, and Legal ImplicationsCase Studies – Business Applications
Key FeaturesGain in-depth insight into LLMs, from architecture through to deploymentLearn through practical insights into real-world case studies and optimization techniquesGet a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications.
You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP.
By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.
What you will learnExplore the architecture and components of contemporary LLMsExamine how LLMs reach decisions and navigate their decision-making processImplement and oversee LLMs effectively within your organizationMaster dataset preparation and the training process for LLMsHone your skills in fine-tuning LLMs for targeted NLP tasksFormulate strategies for the thorough testing and evaluation of LLMsDiscover the challenges associated with deploying LLMs in production environmentsDevelop effective strategies for integrating LLMs into existing systemsWho this book is forIf you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.
Table of ContentsLLM ArchitectureHow LLMs Make DecisionsThe Mechanics of Training LLMsAdvanced Training StrategiesFine-Tuning LLMs for Specific ApplicationsTesting and Evaluating LLMsDeploying LLMs in ProductionStrategies for Integrating LLMsOptimization Techniques for PerformanceAdvanced Optimization and EfficiencyLLM Vulnerabilities, Biases, and Legal ImplicationsCase Studies – Business Applications
725 pages, Kindle Edition
Published October 31, 2024
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About the author
Irena Cronin
12 books7 followersIrena Cronin is SVP of Product for DADOS Technology which is creating a data analytics and visualization platform for the Apple Vision Pro. She is also the CEO of Infinite Retina which provides research to help companies develop and implement AI, AR and other new technologies for their businesses. Previous to this, she worked for several years as an equity research analyst and gained extensive experience in evaluating both public and private companies.
Cronin has a Joint MBA/MA from the University of Southern California and an MS in Management and Systems from New York University. She graduated with a BA from the University of Pennsylvania with a major in Economics (summa cum laude). She has near-fluent proficiency in Mandarin, intermediate in Japanese and beginning in Korean.
Cronin has a Joint MBA/MA from the University of Southern California and an MS in Management and Systems from New York University. She graduated with a BA from the University of Pennsylvania with a major in Economics (summa cum laude). She has near-fluent proficiency in Mandarin, intermediate in Japanese and beginning in Korean.
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