Algebra Lineare per Data Science e Machine Learning

Name: Algebra Lineare per Data Science e Machine Learning
Brand: Independently Published
SKU: 9798183645354
Price: 1684.0 INR
Availability: InStock

by Federico Caboni

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ISBN13: 9798183645354
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: June 21, 2026
Pages: 168
Original Price: GBP 14.91
Language: Italian
Edition: N/A
Item Weight: 232 grams
BISAC Subject(s): General

About The Book

L'algebra lineare � una materia fondamentale che costituisce il linguaggio formale della scienza dei dati e della modellizzazione statistica contemporanea. In un panorama tecnologico dominato dall'elaborazione di dataset multidimensionali, la comprensione profonda delle strutture algebriche non � pi� un mero requisito teorico, ma una necessit� metodologica per garantire la robustezza e l'efficacia degli algoritmi di apprendimento automatico.

Questo volume offre una trattazione sistematica e rigorosa dei principi dell'algebra lineare, strutturata per rispondere alle esigenze computazionali della Data Science e del Machine Learning. Il percorso didattico si snoda attraverso una progressione logica che parte dall'aritmetica delle matrici e dei tensori per giungere alle tecniche avanzate di decomposizione spettrale e calcolo tensoriale.

I nostri nuclei tematici:

Fondamenti e Strutture: Spazi vettoriali, indipendenza lineare, basi e trasformazioni matriciali intese come operatori di informazione.
Analisi Spettrale e Riduzione della Dimensionalit� Una disamina approfondita di autovalori, autovettori e della Singular Value Decomposition (SVD) come strumenti cardine per l'estrazione di feature e la compressione dei dati.
Ottimizzazione e Minimi Quadrati: L'applicazione delle proiezioni ortogonali e del calcolo matriciale alla risoluzione di problemi di regressione e alla minimizzazione delle funzioni di costo.
Algebra Lineare Applicata: L'integrazione strutturale del calcolo matriciale nelle reti neurali profonde e l'analisi della stabilit� numerica per l'implementazione di sistemi su larga scala.

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