SKU/Artículo: AMZ-B0GGPL1LTW
Machine Learning Algorithms: From Classical Methods to Deep Neural Networks: Supervised, Unsupervised, and High-Dimensional Learning
Format:
Paperback
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En stock
En stock
Peso con empaque:
0.28 kg
0.28 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Discover the principles and algorithms that power modern machine learning in this comprehensive guide. From foundational concepts to advanced techniques, the book explores supervised, unsupervised, and high-dimensional learning, bridging theory and practice for readers eager to master the essentials and beyond. After a brief introductory part including useful theoretical objects, the k-nearest neighbor (kNN) algorithm is presented and its properties analyzed. In particular the performance with respect to the data dimension is discussed which motivates in turn the ridge (L2) and Lasso (L1) regularization in the context of linear and logistic regression. Armed with these tools, the focus of the presentation goes to algorithm tailored for high dimensions: stochastic optimization, deep neural networks but also Bayesian classification and unsupervised methods such as the k-means The book is complemented by exercises and computer implementations in Python. This Master's level textbook builds from a course that the author has been teaching at Université Paris Dauphine - PSL and is aimed at students, researchers, and professionals working in the general topic of machine learning.
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AR$79.173
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AR$35.985
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