V Amala Deepa (42 results)

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: 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. …

Paperback. Condition: new. Paperback. 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. 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.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Ce livre est une feuille de route orientee vers la recherche a travers l'apprentissage automatique moderne, structuree selon trois axes: perspectives de recherche, avancees recentes et orientations futures. La partie I etablit des methodologies rigoureuses, une IA centree sur les donnees (apprentissage actif, supervision faible, attenuation des biais) et une evaluation critique au-dela des metriques uniques. La partie II passe en revue les architectures neuronales (des Transformers aux modeles d'espace d'etats), l'optimisation (SAM, double descente), la modelisation generative (unification de la diffusion), l'apprentissage profond geometrique et l'apprentissage auto-supervise/multimodal. La partie III traite des modeles fondateurs - lois d'echelle, capacites emergentes (apprentissage en contexte, chaine de pensee), frontieres de l'IA generative (hallucination, generation controlable) et approches neuro-symboliques (RAG, utilisation d'outils). La partie IV aborde les grands defis: apprentissage automatique efficace/durable, apprentissage continu/meta-apprentissage, interpretabilite/robustesse/alignement (RLHF, representation causale) et voies vers l'AGI avec un impact societal. Chaque chapitre comprend des questions ouvertes et des listes de controle de recherche pratiques, visant a faire passer les lecteurs du statut de consommateurs a celui de contributeurs. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Ce livre est une feuille de route orientee vers la recherche a travers l'apprentissage automatique moderne, structuree selon trois axes: perspectives de recherche, avancees recentes et orientations futures. La partie I etablit des methodologies rigoureuses, une IA centree sur les donnees (apprentissage actif, supervision faible, attenuation des biais) et une evaluation critique au-dela des metriques uniques. La partie II passe en revue les architectures neuronales (des Transformers aux modeles d'espace d'etats), l'optimisation (SAM, double descente), la modelisation generative (unification de la diffusion), l'apprentissage profond geometrique et l'apprentissage auto-supervise/multimodal. La partie III traite des modeles fondateurs - lois d'echelle, capacites emergentes (apprentissage en contexte, chaine de pensee), frontieres de l'IA generative (hallucination, generation controlable) et approches neuro-symboliques (RAG, utilisation d'outils). La partie IV aborde les grands defis: apprentissage automatique efficace/durable, apprentissage continu/meta-apprentissage, interpretabilite/robustesse/alignement (RLHF, representation causale) et voies vers l'AGI avec un impact societal. Chaque chapitre comprend des questions ouvertes et des listes de controle de recherche pratiques, visant a faire passer les lecteurs du statut de consommateurs a celui de contributeurs. 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.…

Paperback. Condition: new. Paperback. Ce livre est une feuille de route orientee vers la recherche a travers l'apprentissage automatique moderne, structuree selon trois axes: perspectives de recherche, avancees recentes et orientations futures. La partie I etablit des methodologies rigoureuses, une IA centree sur les donnees (apprentissage actif, supervision faible, attenuation des biais) et une evaluation critique au-dela des metriques uniques. La partie II passe en revue les architectures neuronales (des Transformers aux modeles d'espace d'etats), l'optimisation (SAM, double descente), la modelisation generative (unification de la diffusion), l'apprentissage profond geometrique et l'apprentissage auto-supervise/multimodal. La partie III traite des modeles fondateurs - lois d'echelle, capacites emergentes (apprentissage en contexte, chaine de pensee), frontieres de l'IA generative (hallucination, generation controlable) et approches neuro-symboliques (RAG, utilisation d'outils). La partie IV aborde les grands defis: apprentissage automatique efficace/durable, apprentissage continu/meta-apprentissage, interpretabilite/robustesse/alignement (RLHF, representation causale) et voies vers l'AGI avec un impact societal. Chaque chapitre comprend des questions ouvertes et des listes de controle de recherche pratiques, visant a faire passer les lecteurs du statut de consommateurs a celui de contributeurs. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Apprentissage Automatique | Perspectives de Recherche, Avancées Récentes et Orientations Futures | V. Amala Deepa (u. a.) | Taschenbuch | Französisch | 2026 | Editions Notre Savoir | EAN 9786630516388 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Este livro e um roteiro orientado para a investigacao atraves da aprendizagem automatica moderna, estruturado sob tres perspetivas: Perspetivas de Investigacao, Avancos Recentes e Direcoes Futuras. A Parte I estabelece metodologias rigorosas, IA centrada em dados (aprendizagem ativa, supervisao fraca, mitigacao de vieses) e avaliacao critica para alem de metricas unicas. A Parte II analisa arquiteturas neuronais (de Transformers a modelos de espaco de estados), otimizacao (SAM, descida dupla), modelacao generativa (unificacao por difusao), aprendizagem profunda geometrica e aprendizagem auto-supervisionada/multimodal. A Parte III abrange modelos fundacionais - leis de escala, capacidades emergentes (aprendizagem em contexto, cadeia de pensamento), fronteiras da IA generativa (alucinacao, geracao controlavel) e abordagens neuro-simbolicas (RAG, utilizacao de ferramentas). A Parte IV aborda grandes desafios: aprendizagem automatica eficiente/sustentavel, aprendizagem continua/meta-aprendizagem, interpretabilidade/robustez/alinhamento (RLHF, representacao causal) e caminhos para a AGI com impacto societal. Cada capitulo inclui perguntas abertas e listas de verificacao praticas para investigacao, visando transformar os leitores de consumidores em colaboradores. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Questo libro e una tabella di marcia orientata alla ricerca attraverso il moderno apprendimento automatico, strutturata attraverso tre prospettive: Prospettive di Ricerca, Recenti Progressi e Direzioni Future. La Parte I stabilisce metodologie rigorose, IA centrata sui dati (apprendimento attivo, supervisione debole, mitigazione del bias) e valutazione critica oltre le singole metriche. La Parte II esamina le architetture neurali (dai Transformer ai modelli di spazio di stato), l'ottimizzazione (SAM, doppia discesa), la modellazione generativa (unificazione della diffusione), l'apprendimento profondo geometrico e l'apprendimento auto-supervisionato/multimodale. La Parte III copre i modelli fondativi - leggi di scala, abilita emergenti (apprendimento nel contesto, catena di pensiero), frontiere dell'IA generativa (allucinazione, generazione controllabile) e approcci neuro-simbolici (RAG, uso di strumenti). La Parte IV affronta le grandi sfide: apprendimento automatico efficiente/sostenibile, apprendimento continuo/meta-apprendimento, interpretabilita/robustezza/allineamento (RLHF, rappresentazione causale) e percorsi verso l'AGI con impatto sociale. Ogni capitolo include domande aperte e checklist pratiche di ricerca, con l'obiettivo di trasformare i lettori da consumatori a contributori. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Dieses Buch ist ein forschungsorientierter Leitfaden durch das moderne maschinelle Lernen, der unter drei Gesichtspunkten strukturiert ist: Forschungsperspektiven, juengste Fortschritte und zukuenftige Richtungen. Teil I etabliert strenge Methodiken, datenzentrierte KI (aktives Lernen, schwache UEberwachung, Minderung von Vorurteilen) und kritische Evaluierung ueber einzelne Metriken hinaus. Teil II bietet einen UEberblick ueber neuronale Architekturen (von Transformern bis hin zu Zustandsraummodellen), Optimierung (SAM, Double Descent), generative Modellierung (Vereinheitlichung von Diffusionen), geometrisches Deep Learning und selbstueberwachtes/multimodales Lernen. Teil III behandelt Basismodelle (Foundation Models) - Skalierungsgesetze, emergente Faehigkeiten (In-Context-Lernen, Chain-of-Thought), Grenzen der generativen KI (Halluzination, steuerbare Generierung) und neurosymbolische Ansaetze (RAG, Werkzeugnutzung). Teil IV befasst sich mit den grossen Herausforderungen: effizientes/nachhaltiges maschinelles Lernen, kontinuierliches/Meta-Lernen, Interpretierbarkeit/Robustheit/Ausrichtung (RLHF, kausale Repraesentation) und Wege zu AGI mit gesellschaftlichen Auswirkungen. Jedes Kapitel enthaelt offene Fragen und praktische Forschungschecklisten, die darauf abzielen, die Leser von Konsumenten zu Mitgestaltern zu machen. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condition: new. Paperback. Ta ksiazka to zorientowana na badania naukowa mapa drogowa po wspolczesnym uczeniu maszynowym, ustrukturyzowana wedlug trzech perspektyw: Perspektywy Badawcze, Ostatnie Postepy i Przyszle Kierunki. Czesc I ustanawia rygorystyczne metodologie, sztuczna inteligencje zorientowana na dane (aktywne uczenie sie, slaby nadzor, lagodzenie stronniczosci) oraz krytyczna ewaluacje wykraczajaca poza pojedyncze wskazniki. Czesc II przedstawia przeglad architektur neuronalnych (od Transformerow po modele przestrzeni stanow), optymalizacje (SAM, podwojne spadek), modelowanie generatywne (unifikacja dyfuzji), geometryczne glebokie uczenie sie oraz uczenie samo-nadzorowane/multimodalne. Czesc III obejmuje modele fundamentalne - prawa skalowania, umiejetnosci emergencjalne (uczenie sie w kontekscie, lancuch mysli), granice generatywnej AI (halucynacje, kontrolowana generacja) oraz podejscia neurosymboliczne (RAG, wykorzystanie narzedzi). Czesc IV porusza wielkie wyzwania: wydajne/zrownowazone uczenie maszynowe, uczenie sie ciagle/meta-uczenie sie, interpretowalnosc/solidnosc/dopasowanie (RLHF, reprezentacja kauzalna) oraz sciezki do AGI o wplywie spolecznym. Kazdy rozdzial zawiera pytania otwarte i praktyczne listy kontrolne badan, ktorych celem jest przeksztalcenie czytelnikow z konsumentow w tworcow. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Questo libro e una tabella di marcia orientata alla ricerca attraverso il moderno apprendimento automatico, strutturata attraverso tre prospettive: Prospettive di Ricerca, Recenti Progressi e Direzioni Future. La Parte I stabilisce metodologie rigorose, IA centrata sui dati (apprendimento attivo, supervisione debole, mitigazione del bias) e valutazione critica oltre le singole metriche. La Parte II esamina le architetture neurali (dai Transformer ai modelli di spazio di stato), l'ottimizzazione (SAM, doppia discesa), la modellazione generativa (unificazione della diffusione), l'apprendimento profondo geometrico e l'apprendimento auto-supervisionato/multimodale. La Parte III copre i modelli fondativi - leggi di scala, abilita emergenti (apprendimento nel contesto, catena di pensiero), frontiere dell'IA generativa (allucinazione, generazione controllabile) e approcci neuro-simbolici (RAG, uso di strumenti). La Parte IV affronta le grandi sfide: apprendimento automatico efficiente/sostenibile, apprendimento continuo/meta-apprendimento, interpretabilita/robustezza/allineamento (RLHF, rappresentazione causale) e percorsi verso l'AGI con impatto sociale. Ogni capitolo include domande aperte e checklist pratiche di ricerca, con l'obiettivo di trasformare i lettori da consumatori a contributori. 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.…