Store besparelser
Hurtig levering
Gemte
Log ind
0
Kurv
Kurv

Understanding Deep Learning

Af: Simon J.D. Prince Engelsk Hardback

Understanding Deep Learning

Af: Simon J.D. Prince Engelsk Hardback
Tjek vores konkurrenters priser
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

  • Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
  • Short, focused chapters progress in complexity, easing students into difficult concepts 
  • Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
  • Streamlined presentation separates critical ideas from background context and extraneous detail
  • Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible 
  • Programming exercises offered in accompanying Python Notebooks 
Tjek vores konkurrenters priser
Normalpris
kr 898
Fragt: 39 kr
6 - 8 hverdage
20 kr
Pakkegebyr
God 4 anmeldelser på
Tjek vores konkurrenters priser
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

  • Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
  • Short, focused chapters progress in complexity, easing students into difficult concepts 
  • Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
  • Streamlined presentation separates critical ideas from background context and extraneous detail
  • Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible 
  • Programming exercises offered in accompanying Python Notebooks 
Produktdetaljer
Sprog: Engelsk
Sider: 544
ISBN-13: 9780262048644
Indbinding: Hardback
Udgave:
ISBN-10: 0262048647
Udg. Dato: 5 dec 2023
Længde: 40mm
Bredde: 237mm
Højde: 212mm
Forlag: MIT Press Ltd
Oplagsdato: 5 dec 2023
Forfatter(e): Simon J.D. Prince
Forfatter(e) Simon J.D. Prince


Kategori Neurale net og fuzzy systemer


ISBN-13 9780262048644


Sprog Engelsk


Indbinding Hardback


Sider 544


Udgave


Længde 40mm


Bredde 237mm


Højde 212mm


Udg. Dato 5 dec 2023


Oplagsdato 5 dec 2023


Forlag MIT Press Ltd

Vi anbefaler også
Kategori sammenhænge