Deep Dive Learning Frameworks by Kumar Tipu (9 results)
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Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Taschenbuch. Condition: Neu. Deep Dive into Deep Learning Frameworks: A Mathematical Exploration | Rupesh Kumar Tipu | Taschenbuch | 68 S. | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207808458 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | An…bieter: preigu.
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Published by LAP LAMBERT Academic Publishing Jun 2024 2024
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 68 pp. Englisch.
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Seller: Biblios, frankfurt am main, GermanyBiblios
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Published by LAP LAMBERT Academic Publishing Jun 2024 2024
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Seller: buchversandmimpf2000, Emtmannsberg, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'Deep Dive into Deep Learning Frameworks: A Mathematical Exploration' offers an in-depth exploration of the fundamental mathematical concepts that power contemporary deep learning methodologies. This book serves as a comprehensive guide… for researchers, practitioners, and enthusiasts seeking to understand the intricate mathematical underpinnings of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced models like generative adversarial networks (GANs) and autoencoders.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Deep Dive into Deep Learning Frameworks: A Mathematical Exploration' offers an in-depth exploration of the fundamental mathematical concepts that power contemporary deep learning methodologies. This book serves as a comprehensive guide…for researchers, practitioners, and enthusiasts seeking to understand the intricate mathematical underpinnings of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced models like generative adversarial networks (GANs) and autoencoders.
