Knygos.lt klubas Knygos.lt nariams
87,21 €
-30%
Įprastai
124,59 €
Machine Learning Fundamentals
Machine Learning Fundamentals
Knygos.lt klubas Knygos.lt nariams
87,21 €
-30%
Įprastai
124,59 €
  • Išsiųsime per 12–18 d.d.
This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent…

Machine Learning Fundamentals (el. knyga) (skaityta knyga) | knygos.lt

Atsiliepimai

(4.33 Goodreads įvertinimas)

Aprašymas

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

Knygos.lt klubas
Knygos.lt nariams
87,21 €
-30%
Įprastai
124,59 €
Kaina registruotiems pirkėjams
Prisijunkite ir už šią prekę
gausite 1,25 Knygų Eurų!?
Išsiųsime per 12–18 d.d.
Įsigykite dovanų kuponą
Daugiau

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

Atsiliepimai

  • Atsiliepimų nėra
0 pirkėjai įvertino šią prekę.
5
0%
4
0%
3
0%
2
0%
1
0%
(rodomas nebus)