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Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media
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Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media (Artificial Intelligence: Foundations, Theory, and Algorithms)
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Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media
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Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media (Artificial Intelligence: Foundations, Theory, and Algorithms)
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Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media
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Foundation Models for Natural Language Processing
Published by Springer International Publishing AG, CH, 2023
ISBN 10: 3031231929 ISBN 13: 9783031231926
Language: English
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Add to basketPaperback. Condition: New. 1st ed. 2023. This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
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Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media (Artificial Intelligence: Foundations, Theory, and Algorithms)
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Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media
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Foundation Models for Natural Language Processing
Published by Springer International Publishing AG, CH, 2023
ISBN 10: 3031231899 ISBN 13: 9783031231896
Language: English
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Add to basketHardback. Condition: New. 1st ed. 2023. This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
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Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media
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Foundation Models for Natural Language Processing
Published by Springer International Publishing AG, CH, 2023
ISBN 10: 3031231929 ISBN 13: 9783031231926
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Add to basketPaperback. Condition: New. 1st ed. 2023. This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
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Machine Learning, Optimization, and Data Science: 10th International Conference, LOD 2024, Castiglione della Pescaia, Italy, September 22-25, 2024, ... Part III (Lecture Notes in Computer Science)
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Foundation Models for Natural Language Processing
Published by Springer, Berlin|Springer International Publishing|Bundesministerium für Bildung und Forschung|Springer, 2023
ISBN 10: 3031231929 ISBN 13: 9783031231926
Language: English
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Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media
Published by Springer International Publishing, 2023
ISBN 10: 3031231899 ISBN 13: 9783031231896
Language: English
£ 47.83
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models.After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
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Machine Learning, Optimization, and Data Science (Paperback)
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031824865 ISBN 13: 9783031824869
Language: English
Paperback. Condition: new. Paperback. The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 2225, 2024.This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024).The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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£ 56.37
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Foundation Models for Natural Language Processing
Published by Springer, Berlin|Springer International Publishing|Bundesministerium für Bildung und Forschung|Springer, 2023
ISBN 10: 3031231899 ISBN 13: 9783031231896
Language: English
£ 43.25
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