ISBN: 9783032303356 - foundations of machine learning and ai: geometry, probability and optimization: 199 (9 risultati)

Condizione: New.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book builds a single, coherent pathway from linear algebra to probability and statistical learning-the twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use case-denoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching. …

Hardcover. Condizione: Brand New. 588 pages. 6.14x1.25x9.21 inches. In Stock.

Hardcover. Condizione: new. Hardcover. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use casedenoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book builds a single, coherent pathway from linear algebra to probability and statistical learning-the twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use case-denoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching. 558 pp. Englisch. …

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use casedenoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg Englisch. …

Hardcover. Condizione: new. Hardcover. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use casedenoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Hardcover. Condizione: new. Hardcover. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use casedenoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching. This book builds a single, coherent pathway from linear algebra to probability and statistical learningthe twin pillars behind modern Data Science, AI, and ML. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …