9798196989476 - mathematics in deep learning: cnns, transformers, diffusion models, and llms (foundation books) de yang, yin (6 resultados)
Idioma: Inglés
Editorial: Independently Published, 2026
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 37,30
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: Independently Published, 2026
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 29,95
Envío por EUR 8,90Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: Amazon Digital Services LLC - Kdp Mai 2026, 2026
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 49,00
Envío por EUR 66,26Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware - Mathematics in Deep Learning is a practical textbook for readers who have seen neural networks in code and want the mathematics behind them to feel usable, explanatory, and connected to practice.The book builds the habits that make deep learning easier to reason about: tracking tensor shape…s, reading models as parameterized functions, connecting losses to data, understanding optimization, and checking whether learned rules will behave well away from the training examples.Topics move from foundations to modern systems, including tensors, probability, empirical risk, convolution, backpropagation, CNN architecture, transfer learning, embeddings, sequence models, attention, transformers, autoregressive modeling, reinforcement learning, preference optimization, training theory, vision-language models, object detection, segmentation, generative modeling, speech recognition, and speech generation.Each chapter uses running examples, compact derivations, figures, exercises, and companion SymPy or numerical code to keep the mathematics tied to inspectable computations.For students, engineers, and self-study readers who want to understand deep learning models more clearly, not just run them.
Idioma: Inglés
Editorial: Independently published, 2026
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 29,26
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Condición: New. Print on Demand.
Idioma: Inglés
Editorial: Independently Published, 2026
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 33,89
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Mathematics in Deep Learning is a practical textbook for readers who have seen neural networks in code and want the mathematics behind them to feel usable, explanatory, and connected to practice.The book builds the habits that make deep learning easier to reason about: tracking tensor shapes…, reading models as parameterized functions, connecting losses to data, understanding optimization, and checking whether learned rules will behave well away from the training examples.Topics move from foundations to modern systems, including tensors, probability, empirical risk, convolution, backpropagation, CNN architecture, transfer learning, embeddings, sequence models, attention, transformers, autoregressive modeling, reinforcement learning, preference optimization, training theory, vision-language models, object detection, segmentation, generative modeling, speech recognition, and speech generation.Each chapter uses running examples, compact derivations, figures, exercises, and companion SymPy or numerical code to keep the mathematics tied to inspectable computations.For students, engineers, and self-study readers who want to understand deep learning models more clearly, not just run them. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Independently Published, 2026
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,21
Envío por EUR 43,15Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Mathematics in Deep Learning is a practical textbook for readers who have seen neural networks in code and want the mathematics behind them to feel usable, explanatory, and connected to practice.The book builds the habits that make deep learning easier to reason about: tracking tensor shapes…, reading models as parameterized functions, connecting losses to data, understanding optimization, and checking whether learned rules will behave well away from the training examples.Topics move from foundations to modern systems, including tensors, probability, empirical risk, convolution, backpropagation, CNN architecture, transfer learning, embeddings, sequence models, attention, transformers, autoregressive modeling, reinforcement learning, preference optimization, training theory, vision-language models, object detection, segmentation, generative modeling, speech recognition, and speech generation.Each chapter uses running examples, compact derivations, figures, exercises, and companion SymPy or numerical code to keep the mathematics tied to inspectable computations.For students, engineers, and self-study readers who want to understand deep learning models more clearly, not just run them. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
