Synopsis
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.
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Complete Handbook of Deep Learning
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Complete Handbook of Deep Learning
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209273858
ISBN 13: 9786209273858
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Complete Handbook of Deep Learning: CNNs, RNNs & GENERATIVE MODELS
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Complete Handbook of Deep Learning (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6209273858
ISBN 13: 9786209273858
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Paperback. Condition: 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 multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209273858
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COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS
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ISBN 10: 6209273858
ISBN 13: 9786209273858
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Complete Handbook of Deep Learning (Paperback)
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ISBN 10: 6209273858
ISBN 13: 9786209273858
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Paperback. Condition: 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. Seller Inventory # 9786209273858
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Complete Handbook of Deep Learning (Paperback)
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209273858
ISBN 13: 9786209273858
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Paperback. Condition: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9786209273858
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COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209273858
ISBN 13: 9786209273858
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Taschenbuch. Condition: Neu. COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS | Sundaresan K (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209273858 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 134385391
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COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS
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ISBN 10: 6209273858
ISBN 13: 9786209273858
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Taschenbuch. Condition: 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. Seller Inventory # 9786209273858
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COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209273858
ISBN 13: 9786209273858
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering. Seller Inventory # 9786209273858