Isbn: 9786630342239 - exploring deep learning models: concepts and applications (5 Ergebnisse)

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 260 pp. Englisch.

Taschenbuch. Zustand: Neu. Exploring Deep Learning Models: Concepts and Applications | Souradeep Sarkar (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630342239 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Exploring Deep Learning Models: Concepts and Applications provides a comprehensive introduction to deep learning, covering its fundamentals, mathematical foundations, neural network architectures, and real-world applications. The book explains key models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), Autoencoders, Generative Adversarial Networks (GANs), and Transformers. It combines theoretical concepts with practical Python implementations using TensorFlow, Keras, and PyTorch. The book also explores applications in computer vision, natural language processing, healthcare, finance, agriculture, robotics, IoT, and smart systems, while discussing emerging topics such as Explainable AI (XAI), transfer learning, and ethical AI, making it a valuable resource for students, researchers, educators, and professionals.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 260 pp. Englisch.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Exploring Deep Learning Models: Concepts and Applications provides a comprehensive introduction to deep learning, covering its fundamentals, mathematical foundations, neural network architectures, and real-world applications. The book explains key models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), Autoencoders, Generative Adversarial Networks (GANs), and Transformers. It combines theoretical concepts with practical Python implementations using TensorFlow, Keras, and PyTorch. The book also explores applications in computer vision, natural language processing, healthcare, finance, agriculture, robotics, IoT, and smart systems, while discussing emerging topics such as Explainable AI (XAI), transfer learning, and ethical AI, making it a valuable resource for students, researchers, educators, and professionals.…