Deep learning explained research par rakesh pathak (4 résultats)
- Couverture souple
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 58,76
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Etat : New.
- Couverture rigide
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 67,66
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Etat : New.
- Couverture souple
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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EUR 84,00
EUR 64,25 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : Neu. Neuware - Deep Learning Explained: Research Applications and Future Innovation presents a comprehensive journey from fundamental concepts to advanced research and future trends in deep learning, beginning with the foundations of artificial intelligence, mathematical principles, and neural network basics,… and progressing through core architectures such as deep feedforward networks, convolutional neural networks, recurrent models, and transformer-based systems. The book emphasizes research methodologies, training strategies, evaluation, and reproducibility, followed by in-depth exploration of real-world applications in healthcare, natural language processing, computer vision, finance, and cybersecurity. It also addresses ethical considerations, challenges, and limitations of deep learning, while highlighting emerging innovations such as self-supervised learning, edge AI, and explainable models, concluding with future research directions, case studies, and pathways for translating academic research into impactful technological innovation.
- Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 99,18
EUR 65,68 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Buch. Etat : Neu. Neuware - Deep Learning Explained: Research Applications and Future Innovation presents a comprehensive journey from fundamental concepts to advanced research and future trends in deep learning, beginning with the foundations of artificial intelligence, mathematical principles, and neural network basics, and pr…ogressing through core architectures such as deep feedforward networks, convolutional neural networks, recurrent models, and transformer-based systems. The book emphasizes research methodologies, training strategies, evaluation, and reproducibility, followed by in-depth exploration of real-world applications in healthcare, natural language processing, computer vision, finance, and cybersecurity. It also addresses ethical considerations, challenges, and limitations of deep learning, while highlighting emerging innovations such as self-supervised learning, edge AI, and explainable models, concluding with future research directions, case studies, and pathways for translating academic research into impactful technological innovation.
