ISBN: 9786209273858 - complete handbook of deep learning: cnns, rnns & generative models (10 resultados)

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

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

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 196 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book offers a comprehensive and structured introduction to the foundations, architectures, and applications of deep learning. Beginning with core mathematical concepts such as linear algebra, probability, and optimization, it builds a strong base for understanding modern neural networks. The text explores key ideas like model capacity, bias-variance trade-off, overfitting, and hyperparameter tuning. Readers are then guided through major deep learning architectures, including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling, and advanced generative models like Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Each chapter presents clear explanations, diagrams, and practical examples to simplify complex concepts. Designed for students, educators, and AI practitioners, the book provides both theoretical depth and practical insights. It serves as a complete reference for anyone seeking to understand, build, and apply deep learning models effectively across real-world problems in computer vision, natural language processing, and generative AI. …

Paperback. Condición: new. Paperback. This book offers a comprehensive and structured introduction to the foundations, architectures, and applications of deep learning. Beginning with core mathematical concepts such as linear algebra, probability, and optimization, it builds a strong base for understanding modern neural networks. The text explores key ideas like model capacity, bias-variance trade-off, overfitting, and hyperparameter tuning. Readers are then guided through major deep learning architectures, including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling, and advanced generative models like Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Each chapter presents clear explanations, diagrams, and practical examples to simplify complex concepts. Designed for students, educators, and AI practitioners, the book provides both theoretical depth and practical insights. It serves as a complete reference for anyone seeking to understand, build, and apply deep learning models effectively across real-world problems in computer vision, natural language processing, and generative AI. 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. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a comprehensive and structured introduction to the foundations, architectures, and applications of deep learning. Beginning with core mathematical concepts such as linear algebra, probability, and optimization, it builds a strong base for understanding modern neural networks. The text explores key ideas like model capacity, bias-variance trade-off, overfitting, and hyperparameter tuning. Readers are then guided through major deep learning architectures, including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling, and advanced generative models like Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Each chapter presents clear explanations, diagrams, and practical examples to simplify complex concepts. Designed for students, educators, and AI practitioners, the book provides both theoretical depth and practical insights. It serves as a complete reference for anyone seeking to understand, build, and apply deep learning models effectively across real-world problems in computer vision, natural language processing, and generative AI.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch. …

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.