Isbn: 9781041152132 - multimodal artificial intelligence and large language models: a comprehensive guide from theory to practice (7 risultati)

Condizione: New.

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

Condizione: 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. Condizione: 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.…

Hardcover. Condizione: 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. Condizione: 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. Condizione: 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.…