Name: Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models (en Inglés)
Price: 127.53 USD
Availability: InStock
Author: Sergios Theodoridis
ISBN: 9780443292385

portada Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models (en Inglés)

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models (en Inglés)

Formato

Libro Físico

Editorial

Academic Press

Autor

Sergios Theodoridis

Idioma

Inglés

N° páginas

1200

Encuadernación

Tapa Blanda

ISBN13

9780443292385

N° edición

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models (en Inglés)

Sergios Theodoridis (Autor) · Academic Press · Tapa Blanda

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Reseña del libro "Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models (en Inglés)"

Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.