Isbn: 9786630120288 - machine learning foundations, algorithms, and applications (7 resultados)

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

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

Condición: New.

Paperback. Condición: new. Paperback. This book provides a comprehensive introduction to Machine Learning, covering both fundamental concepts and advanced techniques. It begins with the basics of machine learning, including supervised, unsupervised, and reinforcement learning, followed by the mathematical foundations such as linear algebra, probability, statistics, calculus, and information theory. The book then explores key machine learning algorithms including regression, classification, clustering, ensemble learning, neural networks, deep learning, CNNs, RNNs, transformers, large language models, GANs, diffusion models, reinforcement learning, and graph neural networks. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 152 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive introduction to Machine Learning, covering both fundamental concepts and advanced techniques. It begins with the basics of machine learning, including supervised, unsupervised, and reinforcement learning, followed by the mathematical foundations such as linear algebra, probability, statistics, calculus, and information theory. The book then explores key machine learning algorithms including regression, classification, clustering, ensemble learning, neural networks, deep learning, CNNs, RNNs, transformers, large language models, GANs, diffusion models, reinforcement learning, and graph neural networks. …

Paperback. Condición: new. Paperback. This book provides a comprehensive introduction to Machine Learning, covering both fundamental concepts and advanced techniques. It begins with the basics of machine learning, including supervised, unsupervised, and reinforcement learning, followed by the mathematical foundations such as linear algebra, probability, statistics, calculus, and information theory. The book then explores key machine learning algorithms including regression, classification, clustering, ensemble learning, neural networks, deep learning, CNNs, RNNs, transformers, large language models, GANs, diffusion models, reinforcement learning, and graph neural networks. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…