Large Language Models in Finance. A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance Miquel Noguer i Alonso

angielski

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Large Language Models in Finance. A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance Miquel Noguer i Alonso - okladka książki

Large Language Models in Finance. A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance Miquel Noguer i Alonso - audiobook MP3

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Autor:: Miquel Noguer i Alonso
Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 554
Dostępne formaty:: PDF

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Do przechowalni

“This hands-on guide has given me valuable insights into building LLM systems, from data quality and model performance to applying RAG, fine-tuning, and AI agents in real-world financial use cases. I highly recommend it!”

Peggy Tsai, Executive Director, Data & AI/ML Enablement, JPMorganChase

Large language models are transforming finance, but production use requires more than prompting: numerical reasoning, timely data, leakage prevention, auditability, and regulatory and model-risk controls.

LLMs in Finance guides you through designing, evaluating, governing, and deploying financial LLM systems. Explore transformers, embeddings, attention, prompting, RAG, and fine-tuning, then apply them to investment research, trading, banking, fraud detection, credit, KYC, AML, compliance, and document intelligence.

Learn to build financial agents using tools, memory, retrieval, orchestration, and human oversight. Address time-series use cases, backtesting contamination, hallucinations, temporal validation, model risk, monitoring, and regulatory expectations.

By the end of this book, you'll have gained the practical skills to build scalable, auditable, production-ready LLM systems that meet business and regulatory requirements, supported by hands-on Python examples, real-world case studies, and a companion GitHub repository.

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O autorze książki

Miquel Noguer i Alonso is a seasoned finance professional with over 30 years of experience in quantitative finance. He co-founded the Artificial Intelligence Finance Institute (AIFI) and has held senior positions at leading financial institutions, including serving as Executive Director at UBS and Chief Investment Officer at Andbank. Miquel is also an active academic, teaching AI and fintech courses at NYU, Columbia University, and other top institutions. He holds an MBA and a degree in business administration from ESADE, as well as a PhD in quantitative finance from UNED. Miquel has published extensively on AI in finance and is a co-editor of the Journal of Machine Learning in Finance

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Szczegóły książki

Tytuł oryginału:: Large Language Models in Finance. A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance
ISBN Ebooka:: 978-18-370-2452-0, 9781837024520
Data wydania ebooka :: 2026-07-30 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku Pdf:: 7.2MB
Rozmiar pliku ePub:: 7.7MB

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Kategorie:
Sztuczna inteligencja » Prompt Engineering
Dane » Data science » Machine Learning
Dane » Data science » NLP

Dostępność produktu

Produkt nie został jeszcze oceniony pod kątem ułatwień dostępu lub nie podano żadnych informacji o ułatwieniach dostępu lub są one niewystarczające. Prawdopodobnie Wydawca/Dostawca jeszcze nie umożliwił dokonania walidacji produktu lub nie przekazał odpowiednich informacji na temat jego dostępności.

Spis treści książki

1. Introduction to Large Language Models
2. Foundations and System Design of Financial LLMs
3. Fine-Tuning LLMs for Finance
4. Retrieval-Augmented Generation for Financial Tasks
5. Architectures and Applications of LLM Agents in Finance
6. Model Context Protocol and Hardened Tool Invocation in Financial Systems
7. Applications of LLMs in Finance
8. Financial Documents and Advisory
9. Reinforcement Learning in LLMs
10. Infrastructure and Performance
11. Ethics, Governance, and Compliance: A Comprehensive Framework for Financial LLMs
12. Mathematical Innovations and Empirical Evidence
13. Advanced Topics: Reasoning, Multimodality, and Time Series LLMs in Finance
14. The Generative Frontier: From Predictive Models to Autonomous Economic Agents
15. Technical Foundations and Practical Resources

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