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…
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.
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.
Atsiliepimai
Atsiliepimų nėra
0 pirkėjai įvertino šią prekę.
5
0%
4
0%
3
0%
2
0%
1
0%
Kainos garantija
Ženkliuku „Kainos garantija” pažymėtoms prekėms Knygos.lt garantuoja geriausią kainą. Jei identiška prekė kitoje internetinėje parduotuvėje kainuoja mažiau - kompensuojame kainų skirtumą. Kainos lyginamos su knygos.lt nurodytų parduotuvių sąrašu prekių kainomis. Knygos.lt įsipareigoja kompensuoti kainų skirtumą pirkėjui, kuris kreipėsi „Kainos garantijos” taisyklėse nurodytomis sąlygomis. Sužinoti daugiau
Elektroninė knyga
22,39 €
DĖMESIO!
Ši knyga pateikiama ACSM formatu. Jis nėra tinkamas įprastoms skaityklėms, kurios palaiko EPUB ar MOBI formato el. knygas.
Svarbu! Nėra galimybės siųstis el. knygų jungiantis iš Jungtinės Karalystės.
Tai knyga, kurią parduoda privatus žmogus. Kai apmokėsite užsakymą, jį per 7 d. išsiųs knygos pardavėjas . Jei to pardavėjas nepadarys laiku, pinigai jums bus grąžinti automatiškai.
Šios knygos būklė nėra įvertinta knygos.lt ekspertų, todėl visa atsakomybė už nurodytą knygos kokybę priklauso pardavėjui.
Atsiliepimai