ISBN: 9786630210927 - smart python for machine learning and intelligent systems (9 resultados)

Condición: New.

Paperback. Condición: new. Paperback. Smart Python for Machine Learning and Intelligent Systems: Deep Learning, Transfer Learning, and AI Engineering Part 2 extends the foundations established in Part 1 by introducing the modern techniques that drive today's intelligent systems. The book provides a practical, implementation-oriented approach to deep learning with Python, covering neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, GRUs, and generative adversarial networks (GANs). It also explores transfer learning through feature reuse, fine-tuning, and domain adaptation, followed by advanced deep learning architectures including ResNet and other state-of-the-art models. The final chapters focus on AI engineering, model optimization, deployment, inference benchmarking, pruning, quantization, and the development of efficient production-ready intelligent systems. Throughout the book, theoretical concepts are reinforced with complete Python implementations, practical experiments, performance evaluation, and real-world case studies. Together with Part 1, this volume provides a comprehensive guide to modern machine learning, deep learning, and AI engineering using Python. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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 324 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Smart Python for Machine Learning and Intelligent Systems: Deep Learning, Transfer Learning, and AI Engineering | Part 2 extends the foundations established in Part 1 by introducing the modern techniques that drive today's intelligent systems. The book provides a practical, implementation-oriented approach to deep learning with Python, covering neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, GRUs, and generative adversarial networks (GANs). It also explores transfer learning through feature reuse, fine-tuning, and domain adaptation, followed by advanced deep learning architectures including ResNet and other state-of-the-art models. The final chapters focus on AI engineering, model optimization, deployment, inference benchmarking, pruning, quantization, and the development of efficient production-ready intelligent systems. Throughout the book, theoretical concepts are reinforced with complete Python implementations, practical experiments, performance evaluation, and real-world case studies. Together with Part 1, this volume provides a comprehensive guide to modern machine learning, deep learning, and AI engineering using Python.…

Taschenbuch. Condición: Neu. Smart Python for Machine Learning and Intelligent Systems | Deep Learning, Transfer Learning, and AI Engineering Part 2 | Alexander I. Iliev | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630210927 | 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. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware 324 pp. Englisch.

Paperback. Condición: new. Paperback. Smart Python for Machine Learning and Intelligent Systems: Deep Learning, Transfer Learning, and AI Engineering Part 2 extends the foundations established in Part 1 by introducing the modern techniques that drive today's intelligent systems. The book provides a practical, implementation-oriented approach to deep learning with Python, covering neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, GRUs, and generative adversarial networks (GANs). It also explores transfer learning through feature reuse, fine-tuning, and domain adaptation, followed by advanced deep learning architectures including ResNet and other state-of-the-art models. The final chapters focus on AI engineering, model optimization, deployment, inference benchmarking, pruning, quantization, and the development of efficient production-ready intelligent systems. Throughout the book, theoretical concepts are reinforced with complete Python implementations, practical experiments, performance evaluation, and real-world case studies. Together with Part 1, this volume provides a comprehensive guide to modern machine learning, deep learning, and AI engineering using Python. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…