Large Language Models: From Theory to Production

This book provides a technically rigorous yet accessible guide to Large Language Models (LLMs), charting their evolution from academic research projects into critical infrastructure for industries as diverse as finance, healthcare, and law. It offers readers a strong grounding in the conceptual foundations of machine learning and deep neural networks before moving into the architectures and methods that define today’s LLMs, including Transformers, tokenization strategies, and pre-training dynamics.
Building on these foundations, the volume engages with the three central frontiers of LLM research: reasoning, alignment, and deployment. It examines structured reasoning approaches such as Tree of Thoughts and multi-agent systems, explores mechanisms for responsible alignment including reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), and provides practical strategies for large-scale deployment and inference efficiency in cloud environments. Alongside these advanced topics, the book highlights emerging methods like Parameter-Efficient Fine-Tuning (PEFT), Retrieval-Augmented Generation (RAG), and prompting innovations.
Beyond text generation, dedicated chapters address LLMs in specialized and forward-looking domains, such as time series forecasting, domain-specific customization, and multimodal systems that integrate perception, reasoning, and action to form "unified cognitive agents." Written for developers, researchers, students, and policymakers alike, this book functions both as a comprehensive reference and as a forward-looking framework for engaging with the next era of AI-driven systems.

Table des matières

1 Introduction to Large Language Models.- 2 Large Language Models Foundations.- 3 Pre-training and datasets Challenges.- 4 Fine-Tuning: Specialization and Techniques.- 5 Building Large Language Models.- 6 Prompt and Context Engineering.- 7 LLM reasoning.- 8 Retrieval in Large Language Models.- 9 The Model Context Protocol.- 10 Large Language Model Agents: Foundations and Engineering Bridge.- 11 Agentic Frameworks (From Theory to Production).- 12 Evaluation of Large Language Models.- 13 Alignment and Safety of Large Language Models.- 14 Deploying LLM solutions: An AWS Example.- 15 Time Series LLMs.- 16 Multimodality.

A propos de l'auteur

Miquel Noguer i Alonso is a seasoned financial professional and academic with over 30 years of experience in the industry. He is the Founder of the Artificial Intelligence Finance Institute, Head of AI at Captide, and Head of Development at Global AI. His career includes roles such as Executive Director at UBS AG and CIO for Andbank. He has served on the European Investment Committee UBS for a decade. He is on the advisory board of FDP Institute and CFA NY.
Mr. Noguer is also an academic, teaching AI, Big Data, and Fintech at institutions like NYU Courant Institute, NYU Tandon, Columbia University, and ESADE. He pioneered the first Fintech and Big Data course at the London Business School in 2017. He is the author of 100 papers on Artificial Intelligence.
He holds an MBA and a Degree in business administration from ESADE, a PhD in quantitative finance from UNED, and other prestigious certifications. His research interests span asset allocation, machine learning, algorithmic trading, and Fintech. He recently authored the books Artificial Intelligence in Finance (Risk Books) and Quantitative Portfolio Optimization (Wiley).
David Pacheco Aznar is a computational mathematician and data scientist who has lead AI in multiple finance startups. He is also an international guest speaker and author of AI in finance books, notably known for his work in Reinforcement Learning and generative systems for finance.
Esteban Vanegas is a financial professional and academic with expertise in finance, artificial intelligence, and quantitative methods. He is a member of the Artificial Intelligence in Business Committee at Tecnologico de Monterrey, where he works at the Santa Fe campus (CSF), and a Partner at Analytics, Accountancy, and Advisors. His research focuses on time series forecasting, financial prediction, and the application of machine learning to portfolio management and decision-making.
Mr. Vanegas has taught finance, artificial intelligence, and quantitative methods for over six years at institutions such as Universidad de los Andes, Universidad Externado de Colombia, and Corporacion Universitaria Autonoma del Cauca. His academic work and teaching reflect his commitment to advancing innovative approaches at the intersection of finance, data, and technology.He holds a Ph.D. in Management with a specialization in Finance and Artificial Intelligence, a Master of Science in Management Research, and a bachelor’s degree in industrial engineering, all from Universidad de los Andes. He collaborates with academic institutions and industry partners to bridge research and practice, with emphasis on algorithmic trading, forecasting, and AI applications in finance.

Résumé

This book provides a technically rigorous yet accessible guide to Large Language Models (LLMs), charting their evolution from academic research projects into critical infrastructure for industries as diverse as finance, healthcare, and law. It offers readers a strong grounding in the conceptual foundations of machine learning and deep neural networks before moving into the architectures and methods that define today’s LLMs, including Transformers, tokenization strategies, and pre-training dynamics.
Building on these foundations, the volume engages with the three central frontiers of LLM research: reasoning, alignment, and deployment. It examines structured reasoning approaches such as Tree of Thoughts and multi-agent systems, explores mechanisms for responsible alignment including reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), and provides practical strategies for large-scale deployment and inference efficiency in cloud environments. Alongside these advanced topics, the book highlights emerging methods like Parameter-Efficient Fine-Tuning (PEFT), Retrieval-Augmented Generation (RAG), and prompting innovations.
Beyond text generation, dedicated chapters address LLMs in specialized and forward-looking domains, such as time series forecasting, domain-specific customization, and multimodal systems that integrate perception, reasoning, and action to form "unified cognitive agents." Written for developers, researchers, students, and policymakers alike, this book functions both as a comprehensive reference and as a forward-looking framework for engaging with the next era of AI-driven systems.
Practical examples throughout make this an essential reference for developers and engineers building intelligent systems; the comprehensive coverage from foundational principles of deep learning and Transformers to advanced, state-of-the-art topics like agentic frameworks, reasoning, and multimodal systems makes it serve as a textbook for students, and a strategic framework for policymakers navigating the AI landscape. 

Détails du produit

• Auteurs: Miquel Noguer i Alonso, David Pacheco Aznar, Vaneg, Esteban Vanegas
• Edition: Springer International Publishing
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 20.04.2026
• EAN: 9783032131454
• ISBN: 978-3-032-13145-4
• Illustrations: Approx. 300 p.
• Catégories: Sciences naturelles, médecine, informatique, technique > Informatique, ordinateurs > Informatique; machine learning, Maschinelles Lernen, Artificial Intelligence, Deep Learning, Natürliche Sprachen und maschinelle Übersetzung, Multimodality, Natural Language Processing (NLP), Prompt Engineering, natural language generation, Time Series Forecasting, Transformer Architecture, Retrieval-Augmented Generation (RAG), LLM fine tuning, Generative Pretrained Transformer Model, LLM agents, LLM Alignment and Safety