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This book is a research-oriented roadmap through modern ML, structured via three lenses: Research Perspectives, Recent Advances, and Future Directions. Part I establishes rigorous methodologies, data-centric AI (active learning, weak supervision, bias mitigation), and critical evaluation beyond single metrics. Part II surveys neural architectures (Transformers to State-Space models), optimization (SAM, double descent), generative modeling (diffusion unification), geometric deep learning, and self-supervised/multi-modal learning. Part III covers foundation models-scaling laws, emergent abilities (in-context learning, chain-of-thought), generative AI frontiers (hallucination, controllable generation), and neurosymbolic approaches (RAG, tool use). Part IV addresses grand challenges: efficient/sustainable ML, continual/meta-learning, interpretability/robustness/alignment (RLHF, causal representation), and pathways to AGI with societal impact. Each chapter includes open questions and practical research checklists, aiming to move readers from consumers to contributors.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
Ms. V. Amala Deepa, Ms. R. Leelavathi, and Ms. P. Kiruthika are Assistant Professors at Holy Cross College, Trichy. Ms. Amala has 13 years of teaching experience and has qualified SET and UGC-NET. Ms. Leelavathi and Ms. Kiruthika have 3 years of teaching and 5 years of research experience each. All are pursuing their Ph.D. in Computer Science.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Machine Learning
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LAP LAMBERT Academic Publishing Aug 2026, 2026
ISBN 10: 6630332024
ISBN 13: 9786630332025
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 236 pp. Englisch. Codice articolo 9786630332025
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Machine Learning | Research Perspectives, Recent Advances and Future Directions
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630332024
ISBN 13: 9786630332025
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Taschenbuch. Condizione: Neu. Machine Learning | Research Perspectives, Recent Advances and Future Directions | V. Amala Deepa (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630332025 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 136495150
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Machine Learning
Editore:
LAP LAMBERT Academic Publishing Aug 2026, 2026
ISBN 10: 6630332024
ISBN 13: 9786630332025
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is a research-oriented roadmap through modern ML, structured via three lenses: Research Perspectives, Recent Advances, and Future Directions. Part I establishes rigorous methodologies, data-centric AI (active learning, weak supervision, bias mitigation), and critical evaluation beyond single metrics. Part II surveys neural architectures (Transformers to State-Space models), optimization (SAM, double descent), generative modeling (diffusion unification), geometric deep learning, and self-supervised/multi-modal learning. Part III covers foundation models-scaling laws, emergent abilities (in-context learning, chain-of-thought), generative AI frontiers (hallucination, controllable generation), and neurosymbolic approaches (RAG, tool use). Part IV addresses grand challenges: efficient/sustainable ML, continual/meta-learning, interpretability/robustness/alignment (RLHF, causal representation), and pathways to AGI with societal impact. Each chapter includes open questions and practical research checklists, aiming to move readers from consumers to contributors. 236 pp. Englisch. Codice articolo 9786630332025
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Machine Learning : Research Perspectives, Recent Advances and Future Directions
Editore:
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630332024
ISBN 13: 9786630332025
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is a research-oriented roadmap through modern ML, structured via three lenses: Research Perspectives, Recent Advances, and Future Directions. Part I establishes rigorous methodologies, data-centric AI (active learning, weak supervision, bias mitigation), and critical evaluation beyond single metrics. Part II surveys neural architectures (Transformers to State-Space models), optimization (SAM, double descent), generative modeling (diffusion unification), geometric deep learning, and self-supervised/multi-modal learning. Part III covers foundation models-scaling laws, emergent abilities (in-context learning, chain-of-thought), generative AI frontiers (hallucination, controllable generation), and neurosymbolic approaches (RAG, tool use). Part IV addresses grand challenges: efficient/sustainable ML, continual/meta-learning, interpretability/robustness/alignment (RLHF, causal representation), and pathways to AGI with societal impact. Each chapter includes open questions and practical research checklists, aiming to move readers from consumers to contributors. Codice articolo 9786630332025
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