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Algebra Lineare per Data Science e Machine Learning
Paperback
Publisher: Independently Published
Published: Jun 21 2026
Pages: 168
Weight: 0.51
Height: 0.36 Width: 6.00 Depth: 9.00
Language: Italian
L'algebra lineare è 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 è più un mero requisito teorico, ma una necessità 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 Dimensionalità 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 stabilità numerica per l'implementazione di sistemi su larga scala.
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