Multimodal artificial intelligence large (10 résultats)

paperback. Etat : New. Language:Chinese.Paperback.Pub Date:2024-08 Pages:256 Publisher:Publishing House of Electronics Industry This book explains the key technologies and related applications involved in multimodal artificial intelligence. including multimodal feature representation. multimodal collaborative learning. multimodal large models. multimodal understanding. multimodal retrieval. multimodal generation. multimodal interaction and multimodal reasoning.…

Etat : New.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New. L. Ashok Kumar is Principal at Thiagarajar College of Engineering, Madurai, Tamil Nadu, India. He was a Postdoctoral Research Fellow from San Diego State University, California. He has three years of industrial experience and twenty-three years of academ.

Buch. Etat : Neu. Neuware - The book provides a comprehensive technical analysis of multimodal artificial intelligence systems and implementation frameworks. It offers thorough coverage of cross-modal processing methods for use, including speech recognition and automatic image captioning. - It presents a detailed discussion of architecture for integrating text, image, audio, and video modalities, cross-modal processing pipelines, and data fusion techniques. - Showcases real-time synchronization mechanisms across different modalities and scalable design patterns for multimodal systems. - Discusses multimodal emotion recognition using deep Learning techniques, focusing on recent advancements, challenges, and ethical considerations. - Investigates deployment optimization strategies to address issues with latency, resource usage, and scalability of multimodal systems. - Focuses on techniques for performance optimization, memory management, and distributed processing for multimodal workloads using frameworks like PyTorch and TensorFlow. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on the new paradigm of artificial intelligence and systematically introduces the key technologies, foundational models, and typical applications of multimodal large models. To make the technical content more accessible for lower-year undergraduate students and newcomers to the AI field, the book presents each key technical point in an easy-to-understand manner and provides numerous intuitive examples. It deeply analyses the structure and technology of several classic multimodal large models. The aim is to offer readers a clear guide to the technical methods, open-source platforms, and application scenarios of multimodal large models, as well as to provide insights into achieving general artificial intelligence, including cutting-edge technologies such as causal reasoning, world models, embodied intelligence, and multi-agent systems. The book aspires to provide a clear perspective for both academia and industry, helping AI researchers gain a more comprehensive understanding of multimodal large model technologies and the development directions of the next generation of artificial intelligence.The book is divided into five chapters. Chapter 1 explores the most representative large model structures in depth. Chapter 2 provides a thorough analysis of the core technologies of multimodal large models. Chapter 3 introduces several representative multimodal large models. Chapter 4 delves into three typical applications: visual question answering, AI-generated content (AIGC), and embodied intelligence. Chapter 5 discusses feasible approaches to achieving general artificial intelligence.This book is suitable not only as a textbook for senior undergraduate and graduate students in relevant university programs but also as an essential reference for IT professionals. The Chinese version of this book has been selected for the undergraduate textbook series at Sun Yat-sen University.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

Hardcover. Etat : Brand New. 396 pages. 6.30x1.02x9.49 inches. In Stock.

Hardcover. Etat : new. Hardcover. The book provides a comprehensive technical analysis of multimodal artificial intelligence systems and implementation frameworks. It offers thorough coverage of cross-modal processing methods for use, including speech recognition and automatic image captioning.It presents a detailed discussion of architecture for integrating text, image, audio, and video modalities, cross-modal processing pipelines, and data fusion techniques.Showcases real-time synchronization mechanisms across different modalities and scalable design patterns for multimodal systems.Discusses multimodal emotion recognition using deep Learning techniques, focusing on recent advancements, challenges, and ethical considerations.Investigates deployment optimization strategies to address issues with latency, resource usage, and scalability of multimodal systems.Focuses on techniques for performance optimization, memory management, and distributed processing for multimodal workloads using frameworks like PyTorch and TensorFlow.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. The text presents research trends and challenges in developing multimodal artificial intelligence applications and helps in designing interactive applications such as chatbots, text generation, sentiment analysis, entity recognition, and language translation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Etat : new. Hardcover. The book provides a comprehensive technical analysis of multimodal artificial intelligence systems and implementation frameworks. It offers thorough coverage of cross-modal processing methods for use, including speech recognition and automatic image captioning.It presents a detailed discussion of architecture for integrating text, image, audio, and video modalities, cross-modal processing pipelines, and data fusion techniques.Showcases real-time synchronization mechanisms across different modalities and scalable design patterns for multimodal systems.Discusses multimodal emotion recognition using deep Learning techniques, focusing on recent advancements, challenges, and ethical considerations.Investigates deployment optimization strategies to address issues with latency, resource usage, and scalability of multimodal systems.Focuses on techniques for performance optimization, memory management, and distributed processing for multimodal workloads using frameworks like PyTorch and TensorFlow.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. The text presents research trends and challenges in developing multimodal artificial intelligence applications and helps in designing interactive applications such as chatbots, text generation, sentiment analysis, entity recognition, and language translation. 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. The book provides a comprehensive technical analysis of multimodal artificial intelligence systems and implementation frameworks. It offers thorough coverage of cross-modal processing methods for use, including speech recognition and automatic image captioning.It presents a detailed discussion of architecture for integrating text, image, audio, and video modalities, cross-modal processing pipelines, and data fusion techniques.Showcases real-time synchronization mechanisms across different modalities and scalable design patterns for multimodal systems.Discusses multimodal emotion recognition using deep Learning techniques, focusing on recent advancements, challenges, and ethical considerations.Investigates deployment optimization strategies to address issues with latency, resource usage, and scalability of multimodal systems.Focuses on techniques for performance optimization, memory management, and distributed processing for multimodal workloads using frameworks like PyTorch and TensorFlow.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. The text presents research trends and challenges in developing multimodal artificial intelligence applications and helps in designing interactive applications such as chatbots, text generation, sentiment analysis, entity recognition, and language translation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…