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Mathematics in Deep Learning: CNNs, Transformers, Diffusion Models, and LLMs - Couverture souple
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Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Mathematics in Deep Learning: CNNs, Transformers, Diffusion Models, and LLMs (Foundation Books)
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Mathematics in Deep Learning (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Paperback. Etat : 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. N° de réf. du vendeur 9798196989476
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Mathematics in Deep Learning
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798196989476
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Mathematics in Deep Learning
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798196989476
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Mathematics in Deep Learning (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : 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. N° de réf. du vendeur 9798196989476
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Expédition depuis Royaume-Uni vers Etats-Unis
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Mathematics in Deep Learning : CNNs, Transformers, Diffusion Models, and LLMs
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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 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. N° de réf. du vendeur 9798196989476
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EUR 49
Expédition à EUR 66,26
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
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