Application large language model (18 résultats)

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paperback. Etat : New. Paperback. Pub Date: 2024-11 Pages: 264 Publisher: China Railway This book is written for vocational education courses on Artificial Intelligence. It contains 14 lessons. including: Introduction to Artificial Intelligence. Language Models and Their Development. Definition of Large Language Models. Architecture of Large Language Models. Artificial Data Annotation. Pre-training Data for Large Language Models. Organizing the Development of Large Language Models. Intelligent Agents for Large Lang.…

paperback. Etat : New. Paperback. Pub Date: 2025-03 Pages: 280 Publisher: Foreign Language Teaching and Research Press The continuous advancement of generative artificial intelligence technology is bringing profound changes to foreign language teaching and research. This book is an introductory text to the Large Language Model (MLM) prompting project. aiming to explore the systematic application of MLM in foreign language teaching and research. thereby promoting the quality improvement and upgrading of foreign lang.…

paperback. Etat : New. Language:Chinese.Paperback. Pub Date: 2024-06 Pages: 344 Publisher: Publishing House of Electronic Industry This book is divided into five parts: Getting Started. Application Practice. Advanced Principles. Case Practice. and Future Expansion. It will start from how AIGC broke out. what related industries it inspired. and AIGC-related technologies that have become popular on the Internet. such as ChatGPT chat application. MidJourney drawing tool. DID digital human. etc. In addition. through ap.…

paperback. Etat : New. Paperback. Pub Date: 2024-05 Pages: 360 Publisher: Publishing House of Electronic Industry This book is a very friendly guide to the application of large language models for AI novice readers. It has two major features: First. it explains complex concepts in an easy-to-understand way. and explains the work and workflow of large language models and basic usage methods through examples and cases. including the three commonly used interaction formats of large language models. prompt engineering.…

paperback. Etat : New. Paperback. Pub Date: 2026-07 Pages: 332 Publisher: China Machine Press This book focuses on the application of large language models in the financial field. systematically introducing its basic theories. key technologies. and practical applications. including accounting. financial analysis. management accounting. investment and risk management. The content covers pre-training and fine-tuning of large language models. accounting data processing and knowledge graph construction. etc. demonstra. …

Hardcover. Etat : new. Hardcover. This Reprint presents a curated collection of nine articles from the Systems Special Issue "AI-Augmented Human-Machine Systems: Engineering and Design in Education", launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Etat : new. Hardcover. This Reprint presents a curated collection of nine articles from the Systems Special Issue "AI-Augmented Human-Machine Systems: Engineering and Design in Education", launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Etat : new. Hardcover. This Reprint presents a curated collection of nine articles from the Systems Special Issue "AI-Augmented Human-Machine Systems: Engineering and Design in Education", launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Etat : Neu. The Application of a Large Language Model (LLM) in Education Reform and Innovation | Buch | Englisch | 2026 | MDPI AG | EAN 9783725885596 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint presents a curated collection of nine articles from the Systems Special Issue 'AI-Augmented Human-Machine Systems: Engineering and Design in Education', launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers.…