Smart python machine learning par iliev alexander (12 résultats)

Etat : New.

Etat : New.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 324 pp. Englisch.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 316 pp. Englisch.

Taschenbuch. Etat : 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. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces the fundamental principles of machine learning and intelligent modeling through practical Python implementations and real-world examples. It begins by establishing the foundations of smart systems, learning paradigms, evaluation metrics, and model validation before progressing to classical supervised and unsupervised machine learning algorithms. Readers are guided through linear regression, logistic regression, k-nearest neighbors, support vector machines, decision trees, ensemble learning, clustering techniques, and principal component analysis, with an emphasis on both the underlying mathematical concepts and their practical implementation using Python. Each chapter combines theoretical background, methodology, implementation, experimental evaluation, and discussion to provide a comprehensive learning experience. Intended for undergraduate and graduate students, researchers, and practitioners, this volume serves as both an academic textbook and a practical reference for developing reliable, interpretable, and data-driven intelligent systems using modern machine learning techniques.…

Paperback. Etat : new. Paperback. This book introduces the fundamental principles of machine learning and intelligent modeling through practical Python implementations and real-world examples. It begins by establishing the foundations of smart systems, learning paradigms, evaluation metrics, and model validation before progressing to classical supervised and unsupervised machine learning algorithms. Readers are guided through linear regression, logistic regression, k-nearest neighbors, support vector machines, decision trees, ensemble learning, clustering techniques, and principal component analysis, with an emphasis on both the underlying mathematical concepts and their practical implementation using Python. Each chapter combines theoretical background, methodology, implementation, experimental evaluation, and discussion to provide a comprehensive learning experience. Intended for undergraduate and graduate students, researchers, and practitioners, this volume serves as both an academic textbook and a practical reference for developing reliable, interpretable, and data-driven intelligent systems using modern machine learning techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : 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.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware 324 pp. Englisch.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware 316 pp. Englisch.