transport gratuit dec21 desk

MAI SUNT 00:00:00:00

MAI SUNT

X

transport gratuit dec21 mob

MAI SUNT 00:00:00:00

MAI SUNT

X

Promotii popup img

TRANSPORT GRATUIT

la orice comanda, prin curier rapid,

oriunde în România!

Pregateste cadourile pentru cei dragi!
Close

Mathematics for Machine Learning

De (autor): Marc Peter Deisenroth

Mathematics for Machine Learning - Marc Peter Deisenroth

Mathematics for Machine Learning


The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Citeste mai mult

transport gratuit

-15%

199.71Lei

234.95 Lei

Sau 19971 de puncte

!

Fiecare comanda noua reprezinta o investitie pentru viitoarele tale comenzi. Orice comanda plasata de pe un cont de utilizator primeste in schimb un numar de puncte de fidelitate, In conformitate cu regulile de conversiune stabilite. Punctele acumulate sunt incarcate automat in contul tau si pot fi folosite ulterior, pentru plata urmatoarelor comenzi.

Livrare in 3-5 saptamani

Descrierea produsului


The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Citeste mai mult

De pe acelasi raft

Noi suntem despre carti, si la fel este si Newsletter-ul nostru.

Aboneaza-te si primesti un cupon de 10% pe care să-l folosesti la urmatoarea ta comanda! In plus, vei afla rapid care sunt promotiile zilei, noutatile si recomandarile noastre.

Ma abonez image one
Ma abonez image one